diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..0f310ccc4156a68fd582c493545dbe3f31b46c34 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +figs/interventions/L20_all_methods.png filter=lfs diff=lfs merge=lfs -text +overleaf_latent_backtracking/figs/confusion_heatmaps.png filter=lfs diff=lfs merge=lfs -text +overleaf_latent_backtracking/figs/curriculum_analysis.png filter=lfs diff=lfs merge=lfs -text +overleaf_latent_backtracking/figs/panelA_stagewise_learning.png filter=lfs diff=lfs merge=lfs -text +overleaf_latent_backtracking/figs/panelE_superposition.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..57d8cbcecc1f9fc698ca102f2fea4ca13d205fec --- /dev/null +++ b/.gitignore @@ -0,0 +1,38 @@ +__pycache__/ +*.py[cod] +*.egg-info/ +.venv/ +wandb/ +# Ignore local checkpoints, but ship known-good L10 stage-0 init +ckpts/** +!ckpts/ +!ckpts/star-coconut-L10-bfs-stage0/ +ckpts/star-coconut-L10-bfs-stage0/** +!ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +**/ckpts/** +!**/ckpts/ +!**/ckpts/star-coconut-L10-bfs-stage0/ +**/ckpts/star-coconut-L10-bfs-stage0/** +!**/ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +logs/ +**/logs/ +data/ +*.out +*.err +*.pt +*.bin +*.safetensors +.DS_Store +.ipynb_checkpoints/ +# large local artifacts +*.pdf +Curriculum_Learning.pdf +latent_backtracking_overleaf.zip +run.py.bak +# keep code figs small optional — ignore bulky plots by default +figs/**/*.png +# exception: intervention figures linked from the README +!figs/interventions/*.png +stage_q_candidates/ckpts/ +stage_q_candidates/logs/ + diff --git a/EXPERIMENT_LOG.md b/EXPERIMENT_LOG.md new file mode 100644 index 0000000000000000000000000000000000000000..d5872e4be76a4a22baba5e7040d7acf7a4d9994b --- /dev/null +++ b/EXPERIMENT_LOG.md @@ -0,0 +1,206 @@ +# Latent-CoT Graph Reachability — Experiment Log + +Persistent working notes so progress/commands survive chat resets. Newest status +at the top of "Current Status"; details and history below. Update this file +whenever we decide/do something. + +Repo: `reasoning-by-superposition-main` (NeurIPS 2025 "Reasoning by Superposition", +authors' official code). Env: conda env `superposition` (torch 2.5.1+cu121, +transformers 4.46.2). Node: `slimgpu` (8× RTX A5000). + +--- + +## Goal + +Small from-scratch GPT-2 (symbol, 2-layer / 8-head / 768-dim) trained with Coconut +continuous latent CoT on a 2-arm **star-graph reachability** task, where the token +vocabulary **is the set of graph node ids** (node id == token id). This makes the +LM head a "node dictionary": applying it (logit lens) to each latent thought reads +out *which graph node that thought points to*, so we can interpret each latent as +one BFS hop of reasoning depth. Then: scale depth (L6 -> L20) and study +backtracking (retrain earlier latent stages when they regress). + +Task detail: two disjoint star components, each with root + 2 arms of length L. +One component is reachable from the query root, the other is a distractor. Model +must answer which candidate leaf is reachable. `bfs_variant: True` => the "frontier" +at hop k is the two reachable-arm nodes at depth k. Key metric = per-hop +`frontier` accuracy (does latent k decode to a correct depth-k reachable node). + +--- + +## Current Status (2026-07-21) + +- **Depth-6 (L6): DONE, success.** Per-hop `frontier` accuracy 0.98-1.0 across all + 6 hops. Logit-lens probe (`probe_latents.py`) confirms each of the 6 latents + decodes to a correct node one BFS hop deeper on the reachable arm, never the + distractor. Checkpoints in `ckpts/star-coconut-L6-bfs/`. Log: + `logs/star_coconut_L6_bfs.log`. +- **Depth-20 (L20), epoch-scheduled version: FAILED to learn.** Config + `args/star_coconut_L20_bfs.yaml`. The curriculum advanced the frontier on a fixed + epoch timer (`scheduled_stage = epoch // epochs_per_stage`) and reached frontier + 14, but per-hop `frontier` acc stayed ~0.02-0.04 at EVERY hop (incl. hop 1). + i.e. training compute got smeared across stages that were never mastered. + **This run was killed.** +- **Depth-20, ACCURACY-GATED version: the current approach.** Config + `args/star_coconut_L20_bfs_accstage.yaml`. Promotion is now driven by measured + accuracy, not the epoch counter (see "Accuracy-gated curriculum" below). + +### 2026-07-21 evening: root-caused the L20 failure = TRAINING DIVERGENCE +- The accuracy-gated run correctly HELD at stage 1 for 65 epochs (~1h) because + hop-1 acc never cleared 0.9 -- but the real issue is that **eval loss was + monotonically INCREASING**: ~4.62 -> 5.9+, while random loss for the 128-vocab is + ln(128) ~= 4.85. So the model started ~chance and diverged past chance. hop-1 acc + ~0.02 == uniform random over ~100 nodes. The old epoch-scheduled L20 run had the + same near-random accuracy => this is an L20 training-instability problem, NOT a + data or curriculum problem. +- Data verified well-formed (root/target/neighbor_k valid, node ids <= 99, 80 edges, + 20 steps). Difference from working L6: ~330-token sequences (80 edges) vs ~6 edges, + wider vocab, `lr=1e-4`, and NO gradient clipping. Classic long-seq divergence. + +### Fixes applied (this session) +1. **Stabilization** in `run.py` + config: gradient clipping (`grad_clip: 1.0`), + lower `lr` 1e-4 -> 3e-5, linear LR `warmup_steps: 200`. +2. **Gate off-by-one fix**: metric "hop k" uses (k-1) latents to predict a depth-k + node, so "stage s learned" == per-hop acc at hop (s+1). The promotion gate now + requires min acc over hops 1..(cur_stage+1) >= threshold (previously 1..cur_stage, + which never checked the current stage's own target). +3. **Limited backprop**: `backprop_depth: 1` (was 6) -- gradient only through the + newest latent step + answer, matching the incremental curriculum. Simple, not + full BPTT across stages. +- Status: relaunch `star_coconut_L20_bfs_accstage.yaml` with these fixes. + +### 2026-07-21 late: first stabilization attempt still diverged -> FSDP clip bug +- With lr 3e-5 + grad_clip 1.0 + warmup, eval loss STILL climbed (4.62 -> 5.2 over + 80 epochs) and stage 1 stayed at chance (HOLD 80 epochs). So clipping wasn't + actually working. +- Root cause: the model is wrapped in **FSDP** (params sharded), but the clip used + `torch.nn.utils.clip_grad_norm_(parallel_model.parameters(), ...)`, which computes + the norm over only each rank's local shard -> under-counts -> effectively no clip. +- Fix: use `parallel_model.clip_grad_norm_()` when the model is FSDP (all-reduces the + global norm). Also lowered `lr` 3e-5 -> 1e-5. +- If loss STILL rises after this, the remaining suspect is model capacity: a 2-layer + GPT-2 doing multi-hop pointer chasing over ~80 shuffled edges (vs ~6 at L6). Next + step would be a controlled L6-with-same-code sanity run, then consider more layers. + +--- + +## Accuracy-gated curriculum + backtracking (what we changed and why) + +Motivation (K's guidance): stage promotion should be **accuracy-dependent**, not +iteration-dependent. Only advance to stage k+1 once every stage 1..k has reached a +target accuracy. And keep the sudoku-style backtracking: at each eval check all +previous stages; if any falls below threshold, go back and rehearse/retrain it. + +Implemented in `run.py`: +- New config flag `accuracy_staging: True`. When on, `scheduled_stage = cur_stage`, + a persistent frontier that only advances when earned. +- After each per-hop eval: promote `cur_stage -> cur_stage+1` only when + `min(frontier_acc[1..cur_stage]) >= promote_threshold` (default 0.9). Because it + checks *all* stages <= frontier, a regression anywhere blocks promotion. +- Logs per-stage timing: `[acc-stage] PROMOTE stage k -> k+1 | solved in N epochs / Ts`. + `HOLD` lines mean the current frontier isn't solved yet. +- Backtracking rehearsal preserved: `bt_r_current` = earliest hop below + `backtrack_detect_threshold`; the training-set builder + `get_graph_latent_cot_dataset_backtrack` samples each example's stage from a + rehearsal distribution (broad w.p. `remember_rate`, else targeted + `[r_current..frontier]`). +- `perhop_val_samples` caps val per-hop eval cost so frequent eval stays cheap. + +Earlier enabling fixes (already in the code): +- **Vectorized latent feedback** in `coconut.py` (clone+scatter instead of a + Python double-loop of ~11k tiny GPU ops). Proven numerically identical. +- **O(batch^2 x depth) sync bug** in `coconut.py` `latent_lists` construction + (per-element `.item()` GPU->CPU syncs) replaced with one `.tolist()` + pure-Python + grouping. This was the real speed killer; latency went from 7357ms -> 561ms/it at + stage 20 (~13x), roughly flat across depth. +- **Truncated BPTT** `backprop_depth: W` in `coconut.py`: detaches fed-back thought + + KV older than W latent steps => bounded backward memory/compute regardless of + depth. Default None = full BPTT (L6 baseline unchanged). L20 uses `backprop_depth: 6`. +- Widened node vocab to 100 (`stokenizer.py` `NUM_NODES=100`), model + `configs/symbol-2layer-8head-768dim-L20.json` `vocab_size=128`, data regenerated + at L=20 (`data/star_2arm_L20_*_fo_bfs.json`, 82 nodes/sample, 80 edges). + +--- + +## Curriculum semantics (what each stage learns) + +From `expand_data`: at **stage s** the prompt has **s latent tokens** and the model +is trained to output the **next hop's frontier node (depth s+1)** -- NOT the final +leaf. Only the final stage (`k = max_steps+1`, all latents + `[A]`) predicts the +target leaf. So each intermediate stage teaches one more hop of the walk: +- stage 0: 0 latents -> predict a depth-1 node (root's neighbor) +- stage 1: 1 latent -> predict a depth-2 node +- stage s: s latents -> predict a depth-(s+1) node +- final: all latents + [A] -> predict the target leaf +Per-hop metric "hop k" = (k-1) latents -> predict depth-k node, i.e. hop k tests +training stage (k-1). (This is why the promotion gate checks hop cur_stage+1.) + +## Interpretability / probing (how latent -> node works) + +- A latent "thought" is the previous position's last-layer hidden state + h in R^768, fed back in place of a token embedding (never discretized). +- Logit lens: node_hat_k = argmax over node-id columns of (W_U h), where W_U is the + model's own tied unembedding. Since node id == token id, this reads the node. +- `probe_latents.py`: one teacher-forced validation forward pass over held-out + graphs; reads the head at each latent-feeder position; scores set-membership in + the hop-k reachable frontier vs the distractor arm. + +--- + +## Commands + +Activate env: +``` +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition +``` + +Launch accuracy-gated L20 (single line; GPUs 2,3): +``` +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Kill a run: +``` +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml' +``` + +Monitor: +``` +grep -E 'acc-stage|scheduled_stage|eval per-hop|Accuracy on validation' logs/star_coconut_L20_bfs_accstage.log | tail -n 40 +``` + +Probe L6 latents (example): +``` +CUDA_VISIBLE_DEVICES=3 python probe_latents.py # see script for args +``` + +--- + +## Known issues / gotchas + +- **GPU 2 has hung on CUDA init** in this session (bad state). Probing all GPUs or + `nvidia-smi` can hang the shell. Always pin `CUDA_VISIBLE_DEVICES` to a known-good + device and wrap ad-hoc probes in `timeout`. (Note: the L20 training itself ran on + 2,3 fine, so 2 may be intermittently OK.) +- **Cursor agent shell repeatedly wedges** behind an unkillable CUDA-init; a window + reload did not always free the agent's worker. Workaround: run launch/kill + commands in a normal terminal; the agent can still read logs/files directly. +- **Multi-line paste hazard:** pasted multi-line blocks lost their newlines and ran + glued together (`superpositioncd ...`). Use the single-line command forms above. +- **Resume-logic crash (fixed):** `run.py` treated any non-empty `ckpts//` + dir as a resume and crashed when no `checkpoint_*` file existed + (`'NoneType'.split`). Fixed to only resume when real `checkpoint_*` files exist. + +--- + +## Next steps + +1. Confirm accuracy-gated L20 starts cleanly and learns hop 1 (stage 1 -> >=0.9). + This is the real test: with all early compute focused on stage 1, does a single + hop become learnable (unlike the smeared epoch-scheduled run)? +2. Record stage-by-stage solve time (epochs + wall-clock) from `[acc-stage]` logs. +3. Watch for backtracking `HOLD`s (earlier stage regressed -> rehearsed). +4. If hop 1 still won't train, debug that specifically (LR, backprop_depth, + data/vocab) rather than advancing the frontier. Consider an L10 bisection run. +5. Re-run `probe_latents.py` on the best L20 checkpoint to see how deep the latents + stay faithful. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..a45a376fb0fcd4a3de06b6c096e62028929a2dcb --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Meta Platforms, Inc. and affiliates. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/NOTES_curriculum_principles.md b/NOTES_curriculum_principles.md new file mode 100644 index 0000000000000000000000000000000000000000..980f357b2c23396e2b7a46a7e9b6f17b54dc8078 --- /dev/null +++ b/NOTES_curriculum_principles.md @@ -0,0 +1,79 @@ +# Operating principles for the latent-curriculum runs + +Working rules for L15/L20 and any deeper/wider graph runs. Follow these before +reaching for a looser gate or killing a run. + +## 1. Tighter gates early pay for themselves later + +Promote only when the balance score is genuinely met. A tight early gate buys +cheaper later stages and fewer backtracks; a loose one lets balance rot behind +the frontier and the debt comes due at depth. + +Do not loosen a threshold to make a run "progress". Progress bought that way is +the failure mode we already diagnosed: frontier@0.95 promotion kept advancing +L15 while ce_score decayed 0.96 -> 0.44 across hops. + +## 2. Early stages are slow; later stages get smooth + +Measured on the L10 ce_score@0.95 arm, the only run that completed all stages: + +| stage | 1->2 | 2->3 | 3->4 | 4->5 | 5->6 | 6->7 | 7->8 | 8->9 | 9->10 | +|-------|------|------|------|------|------|------|------|------|-------| +| epochs| 140 | 45 | 15 | 35 | 10 | 20 | 45 | 20 | 30 | + +Stage 1 cost 140 epochs, ~3x any later stage. Everything after cleared in 10-45. + +Consequence: a run sitting at stage 1 or 2 for ~150 epochs is on trajectory, not +stalled. Judge a run against this curve before concluding anything. The number +of backtracks should also fall as stages advance -- track it as a health signal, +since a rising count is the real warning sign. + +## 3. Stalling usually means more training, not a smaller threshold + +Check in this order before touching the gate: how many epochs has this stage +actually had, relative to the 140-epoch stage-1 reference; is the metric flat or +still creeping; is backtracking firing at all (a gate that never triggers cannot +repair anything). + +## 4. Stage 0 is not the bottleneck + +L15 hop-1 saturates at ce_score ~0.96 by epoch 45 and holds 0.94-0.98 for the +rest of the curriculum. Extra stage-0 epochs buy ~0.016. Do not spend GPU time +there. + +## 5. The point of tightening is depth and branching + +The payoff is deeper graphs with more branches. A recipe that only works by +loosening the gate will not extend, so prefer the tight-gate result even when it +is slower to obtain. + +## 6. Once early latents are robust, W=1 BPTT should be enough + +If stages 0..k-1 are already solid (high ce_score, rarely backtracked), the +forward chain of latents is carrying the right frontier state, so gradients for +stage k only need to flow through the *last* latent recurrence step +(`backprop_depth: 1`). Earlier latents should not need re-learning via full +BPTT -- that is exactly what the long/robust early training bought. + +Implication for experiments: +- Do NOT test W=1 from a cold or weakly-gated start. Our earlier W=1 L15/L20 + runs failed for this reason: early hops were not solid, so truncating BPTT + cut the only path that could still fix them. +- DO test W=1 by warm-starting from a checkpoint where hops 1..k already meet + the tight ce_score gate, then training stage k+1..L with `backprop_depth: 1`. +- The success criterion is: same promote/backtrack behaviour as full BPTT, with + fewer backtracks and similar stage-clearing times. If W=1 needs constant + repair of early hops, early training was not robust enough yet. + +## Fixed setup details worth not rediscovering + +- Mirror the L10 winner: `promote_metric` and `backtrack_metric` both + `ce_score`, both at 0.95, `init_stage: 1`, warm start from the pinned stage-0 + checkpoint. +- Run fp32. `bf16: True` adds mantissa noise to a metric that compares + near-equal log-probabilities between the two arms. +- Default to full BPTT while building the early stages. Switch to W=1 only as + a *transfer* test from a mastered early-stage checkpoint (principle 6). +- Point warm starts at the *latest* stage-0 checkpoint. The diag sweep silently + used a stale `checkpoint_99` (ce_score 0.947) when `checkpoint_150` (0.961) + existed. diff --git a/ONBOARDING.md b/ONBOARDING.md new file mode 100644 index 0000000000000000000000000000000000000000..05ae9926d5de28c8e898fa71bb33da173c79f3bb --- /dev/null +++ b/ONBOARDING.md @@ -0,0 +1,294 @@ +# Onboarding / Collaboration Guide + +This repo (`reasoning-by-superposition`) is where the active **continuous-latent reasoning** +experiments live. This document explains the two codebases in play, exactly what we changed here +vs. the upstream paper code, how to set things up and run experiments, and the git workflow for +contributing. + +If you just want to run something, jump to **[Quick Start](#quick-start)**. If you want to +understand the experiment design, read **[Concepts](#concepts)** first. + +--- + +## 1. The two codebases + +There are two related but separate repos. They share conceptual DNA (Coconut-style continuous +latent chain-of-thought on **directed graph reachability**), but differ in scale, data format, and +which questions they answer. + +| Repo | Where | Base model | Task/data | Role | +|---|---|---|---|---| +| **`reasoning-by-superposition`** (this repo, "rbs") | Berkeley SCF `/scratch/users/gatmiry/reasoning-by-superposition`; backup at `github.com/seyedparsa/reasoning-by-superposition` (private) | 2-layer, 8-head, 768-dim GPT-2, **randomly initialized** (~15M params) | Symbolic graph reachability, integer node tokens 0–30 (`stokenizer.py`) | **Active work.** Small, fast, from-scratch. Where the 2-arm-star + final-only experiments run. | +| **`thoughtformer`** | `github.com/seyedparsa/thoughtformer` | Qwen3-0.6B-Base / GPT-2 (pretrained) | Bridge / Tree-n100 / ProsQA, natural-language premises | The parent project. Larger-scale Coconut fork with a full curriculum/staging framework. See its `CLAUDE.md`. | + +**Lineage.** `thoughtformer` is a fork of the original [Coconut](https://arxiv.org/abs/2412.06769). +`reasoning-by-superposition` is the *authors'* code for the NeurIPS 2025 paper +[Reasoning by Superposition](https://arxiv.org/abs/2505.12514) (also a Coconut descendant); we +cloned it and built our experiments on top. **Our `origin` is the upstream author repo (`Ber666`, +read-only for us); all our work lives on the `mine` remote** — see [Git workflow](#5-git-workflow). + +For the rest of this doc, "we/our" = the changes made on top of the upstream `Ber666` code. + +--- + +## 2. What we changed in this repo (rbs) + +Upstream trains one thing: standard per-hop Coconut on ProsQA. We added a new **task** (2-arm star), +a new **training variant** (final-only), a new **staging algorithm** (revert), per-depth **metrics**, +and a pile of configs/scripts. All of it is additive and flag-gated — the upstream ProsQA path is +byte-identical when the new flags are off. + +### Commit history (our 10 commits on top of `origin/main`) + +``` +fc56a19 Backup: configs, gen/probe/verify/wb scripts, launchers, stokenizer tweak; gitignore data/ +12706bd final-only d1 troubleshoot: diagnostic matrix (lr sweep, small model, high wd, base-coconut control) +ab729fa depth-1 final-only scaling sweep: L=1 data + configs (14k-960k) +ff52afe add revert-staged final-only variant (gates depth on per-depth acc) + configs +282a0f8 final-only: log reachable/frontier/optimal per depth alongside acc (classify [A] output) +8c5fbcd add general final-only reachability variant (per-depth [A] training) + 2-arm-star fo data/configs +64c9172 add 2-arm star dataset generator + standard/revert configs (L=6) +7d06b5f Revert "add hysteresis staging variant ..." <- dead end, reverted +9f6a050 add hysteresis staging variant ... <- dead end +4f2efd3 wandb: resume same run id across preemptions; key eval metrics on train/epoch +``` + +### File-by-file + +- **`generate_2arm_star.py`** — *new.* Generates the 2-arm-star dataset. Emits `neg_root` / + `neg_neighbor_k` (the unreachable component's root and per-depth frontier) alongside the usual + `root` / `neighbor_k`, in **two flavors**: `_coconut` (standard = shortest-path frontier, one node + per depth) and `_bfs` (full BFS frontier). Key funcs: `make_star`, `to_sample(star, flavor)`, + `gen(L, n, rng, seen)`, `main`. +- **`graph_metrics.py`** — *new.* Per-depth evaluation metrics. `perhop_categorize` classifies an + intermediate-node prediction into **reachable / frontier / optimal** (via BFS distances from + root and target). `finalonly_categorize` scores the final `[A]` answer per depth. `_distances`, + `_bfs`, `_classify`, `category_log_dict` are helpers. +- **`dataset.py`** — added **`get_graph_finalonly_dataset`** (the final-only builder). The base + builders (`get_graph_latent_cot_dataset` = standard per-hop, `get_graph_latent_question_dataset`, + `expand_data`, `MyCollator`, etc.) are upstream and untouched. +- **`run.py`** — flag-gated dispatch inserts (all `elif getattr(configs, "final_only", False)` / + `getattr(configs, "revert_staging", False)`): + - **wandb**: resume the same run id across SLURM preemptions; key eval metrics on `train/epoch`. + - **`scheduled_stage`** selection: `revert_staging` branch sets it from `revert_next_stage` + (else the upstream `epoch // epochs_per_stage`). + - **train + val-loss datasets**: route to `get_graph_finalonly_dataset` when `final_only`. + - **metrics block**: `final_only` -> `finalonly_categorize` (logs `eval/acc_hop{k}` etc.); + else upstream `perhop_categorize`. Revert logic: `revert_next_stage = first depth k with + metric < revert_threshold, minus 1` (metric = `acc` for final-only, `frontier` for per-hop). +- **`stokenizer.py`** — minor tweak (symbolic tokenizer; nodes 0–30 + special tokens, vocab 40). +- **`configs/symbol-2layer-8head-768dim.json`** — the from-scratch model definition (GPT-2, 2L/8H/768d). + (`...128dim.json` is a smaller diagnostic variant.) +- **`args/*.yaml`** — all the run configs (see [Configs](#4-configs)). +- **Helper scripts** (not part of the pipeline, but committed for reproducibility): `gen_*.py` + (data generation for specific sweeps), `probe_*.py` / `verify_*.py` / `show_examples.py` (sanity + checks on data + model outputs), `wb_*.py` (wandb metric pulls), `run_repro*.sbatch` (SLURM launchers). + +### What we changed in `thoughtformer` (the parent repo) + +`thoughtformer` already had the full framework we're conceptually mirroring here — methods +(`no_cot` / `cot` / `coconut` / `thoughtformer`), two curriculum axes (`data_curriculum`, +`thought_curriculum`), and three staging modes (`epoch` / `adaptive` / `two_pointers`). Read its +**`CLAUDE.md`** for the complete map. Our recent changes there were limited to plotting/analysis +scripts and result artifacts, not the training core. + +--- + +## 3. Concepts + +### The task: 2-arm star (graph reachability) + +Two **disconnected, mirror-image** star graphs: +- **Reachable component**: root `R` with a *target arm* (length `L`) and a *decoy arm* (length `L`). +- **Unreachable component**: neg-root `R2` with `neg_target` (plays target's role) and a decoy. + +Nodes are integer tokens; a star with arm length `L` has `2 + 4L` nodes. The model reads shuffled +edge premises + two candidate targets + the start root, and must pick the reachable candidate. +`difficulty` / depth = hops from root to target. + +Sequence format (single example): +``` +[Q] cand1 cand2 [R] root <|latent|> ... <|latent|> [A] answer +``` +with `d` latent tokens at depth `d`. + +### Two training variants + +1. **Standard per-hop Coconut** (`get_graph_latent_cot_dataset`, upstream). At stage `s`, supervise + an **intermediate path node** (`neighbor_k[s+1]`) with `s` latents and *no* `[A]`; only the final + stage trains `[A] -> target`. Two flavors: **standard** (`neighbor_k` = shortest-path frontier, + unique node) and **BFS** (`bfs_variant: True`, full frontier). This is the setting the paper + studies; the model learns to traverse the graph in latent space. + +2. **Final-only** (`get_graph_finalonly_dataset`, `final_only: True`, *ours*). At depth `d`, form two + depth-`d` candidates — `reach = choice(neighbor_k[d])`, `neg = choice(neg_neighbor_k[d])` — give + `d` latents + `[A]`, and train only to output `reach`. This **front-loads the reachability + decision to every depth** instead of supervising intermediate nodes. Same standard/BFS flavor + distinction (inherited from the data file). Motivation: isolate whether the model can *decide + reachability* (a 1-bit selection) vs. *traverse* — we found binary final-only supervision tends + to **memorize** and is far more data-hungry than per-hop chain supervision, which generalizes. + +### Staging (when to add latents / advance depth) + +- **Fixed** (upstream): `scheduled_stage = epoch // epochs_per_stage`. +- **Revert** (`revert_staging: True`, *ours*): each eval, `scheduled_stage = (first depth k whose + metric < revert_threshold) - 1`. This **gates advancement on generalization** — depth only grows + once the model actually solves the current depth on held-out data. `revert_metric` = `acc` for + final-only, `frontier` for per-hop. + +### Metrics (logged to wandb per depth `k`) + +- Per-hop runs: `{reachable,frontier,optimal}_hop{k}` — is the predicted intermediate node reachable + from root / on *a* shortest-path frontier / on the *target's* shortest path, respectively. +- Final-only runs: `eval/acc_hop{k}` and `train/acc_hop{k}` (per-depth answer accuracy) + `eval/acc` + (deepest-depth accuracy). `revert/stage` tracks the current scheduled stage. + +--- + +## 4. Configs + +Configs live in `args/*.yaml`. CLI overrides work: `run.py args/foo.yaml lr=3e-4 num_epochs=200`. + +Naming: `star_` = 2-arm star; `prosqa_` = upstream ProsQA task; `diag_` = depth-1 diagnostics. +`_fo` / `finalonly` = final-only variant; `_bfs` = BFS flavor; `_rev` / `revert` = revert staging; +`d1` / `L2` / `L6` = arm length; trailing number = train-set size. + +Key fields: + +| field | meaning | +|---|---| +| `coconut: True` | use the Coconut latent wrapper | +| `c_thought` | latent tokens per reasoning step (`0` = no-cot) | +| `max_latent_stage` | max depth / latent count (arm length `L`) | +| `pad_latent_to_max` | cap latent count at `max_latent_stage` | +| `final_only` | use the final-only training variant (ours) | +| `bfs_variant` | use full BFS frontier instead of shortest-path | +| `revert_staging` | gate depth advancement on generalization (ours) | +| `revert_metric` / `revert_threshold` | metric + bar for revert staging (e.g. `acc` / `0.8`) | +| `epochs_per_stage` | (fixed staging) epochs before advancing a stage | +| `uniform_prob` | prob. of sampling a *shallower* depth than scheduled (curriculum mix) | +| `model_id` | model def, e.g. `configs/symbol-2layer-8head-768dim.json` | +| `train_path` / `val_path` | data files (regenerate with the gen scripts — see below) | +| `lr`, `weight_decay`, `batch_size_training`, `num_epochs`, `seed` | standard knobs | +| `bf16: False` | keep fp32 (bf16 NaNs with ≥3 latents) | +| `save_only_improve` | only checkpoint on val improvement | + +Representative starting points: `star_coconut_full.yaml` (standard per-hop, L=6), +`star_fo_L6_100k_rev.yaml` (final-only + revert, L=6), `star_finalonly_bfs.yaml` (BFS flavor). + +--- + +## Quick Start + +### Environment + +Python 3.12; deps are pinned in `requirements.txt` (torch 2.5.1, transformers 4.46.2, wandb 0.18.7, …). + +```bash +git clone git@github.com:seyedparsa/reasoning-by-superposition.git # the private backup +cd reasoning-by-superposition +conda create -n superposition python=3.12 && conda activate superposition +pip install -r requirements.txt +wandb login # required before training/eval (or set WANDB_MODE=offline) +``` + +On the Berkeley box, the shared env already exists at `/accounts/projects/peter/gatmiry/sup_env` +(no need to rebuild) — just `export PATH="$ENV/bin:$PATH"`. + +### Data is NOT in git — regenerate it + +`data/` is gitignored (regenerable). After cloning, generate what a config needs. E.g. the L=6 +2-arm-star final-only data: + +```bash +python generate_2arm_star.py # writes data/star_2arm_L6_{train,valid,test}_fo_{coconut,bfs}.json +``` + +The `gen_*.py` scripts generate the specific sweep datasets (L=1 size sweep, L2/L3/L6 100k, etc.); +open the one matching your config's `train_path`. Check a config's `train_path`/`val_path` to see +which file it expects, then run the matching generator. + +### Train + +Distributed launch (2 GPUs is the standard setup for the 768-dim model): + +```bash +torchrun --standalone --nproc_per_node 2 run.py args/star_fo_L6_100k_rev.yaml +# CLI overrides: append e.g. lr=3e-4 num_epochs=200 +``` + +On SLURM (Berkeley), use a launcher and set the node: +```bash +sbatch --job-name=my-run run_repro_online_horton.sbatch args/star_fo_L6_100k_rev.yaml +``` +Launchers pin `--gpus-per-node 2` and the conda env by absolute path. Logs land in `logs/%x_%j.out`. +Checkpoints go to `ckpts//` (gitignored). Runs auto-resume from the latest checkpoint on +preemption (same wandb run id). + +### Evaluate + +Set `only_eval: True` and `load_model_path: ` in a config, then launch normally. There are +`eval_*.yaml` configs (e.g. `eval_prosqa_coconut_2l_8h_768d.yaml`) as templates. + +### Sanity-check before a big run + +```bash +python show_examples.py # print tokenized examples for a config's data +python verify_star.py # structural checks on generated star data +``` + +--- + +## 5. Git workflow + +### Remotes + +``` +origin https://github.com/Ber666/reasoning-by-superposition.git # upstream author (READ-ONLY for us) +mine git@github.com:seyedparsa/reasoning-by-superposition.git # our private backup (push here) +``` + +**All our work lives on `mine`.** Never expect to push to `origin`. `mine/main` tracks our +`main` (upstream history + our 10 commits). + +### For a collaborator + +1. **Get access.** The `mine` repo is private — the owner (seyedparsa) must add you as a + collaborator on GitHub (repo Settings → Collaborators). Then clone `mine` (see Quick Start). +2. **Branch for your work** rather than committing straight to `main`: + ```bash + git checkout -b feature/my-experiment + # ... work, commit ... + git push -u origin feature/my-experiment # 'origin' = your clone's default = the mine repo + ``` + Open a PR into `main` on `github.com/seyedparsa/reasoning-by-superposition`. +3. **Don't commit `data/` or `ckpts/`** — both are gitignored (regenerable / large). Commit code, + configs, and small model defs only. +4. **Keeping the Berkeley working copy in sync.** On the shared box the remote is named `mine`: + ```bash + cd /scratch/users/gatmiry/reasoning-by-superposition + git add -A && git commit -m "..." && git push mine main + ``` + +### Pulling upstream changes (rare) + +```bash +git fetch origin && git merge origin/main # if Ber666 ever updates +``` + +--- + +## 6. Gotchas / lessons + +- **fp32 only** — bf16 produces NaNs in the iterative latent feedback with ≥3 latent tokens. + Keep `bf16: False`. +- **Binary final-only memorizes.** Per-hop / chain supervision generalizes at ~14k examples with no + train/eval gap; binary final-only supervision (train→1.0, eval→chance) is far more data-hungry and + the data threshold rises steeply with graph size. This is the central finding driving the current + experiments. +- **`save_only_improve` + preemption livelock**: if val is stuck at chance, a preempted run can + resume from an early "best" checkpoint and never progress. Watch for it on flat-val runs. +- **Berkeley disk**: scratch is quota'd (~20 GB). Data (108 MB) + checkpoints (230 MB+) fill it fast; + clean concluded `ckpts/` and regenerable `data/` when low. +- **`data/` is not backed up** — only code + configs are in git. Regenerate from the gen scripts. +- **Results/metrics** live in wandb (`seyedparsa/coconut` project), not in the repo. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..eba90ae6c796b4c8f8ab7dadc22ac3dc9b3b21d1 --- /dev/null +++ b/README.md @@ -0,0 +1,152 @@ +# Latent CoT curriculum + backtracking (2-arm star) + +Fork / extension of [Reasoning by Superposition](https://arxiv.org/abs/2505.12514) ([original repo](https://github.com/Ber666/reasoning-by-superposition)). + +We train [Coconut](https://arxiv.org/abs/2412.06769)-style continuous chain-of-thought on **2-arm star graph reachability** with: + +- **CE-gated curriculum** over latent depth (promote when per-hop CE score clears a threshold) +- **Backtracking (BT)** when an earlier hop drops below threshold +- **Truncated BPTT** via `backprop_depth` (reported recipe: **W=2**) +- **Latent interventions** that test whether the answer depends on the last thought vs earlier ones + +Repo: https://github.com/Avra98/reasoning-by-superposition-latent + +## Setup + +```bash +git clone https://github.com/Avra98/reasoning-by-superposition-latent.git +cd reasoning-by-superposition-latent +conda create -n superposition python=3.12 +conda activate superposition +pip install -r requirements.txt +``` + +## Reproduce training (L=10 / 15 / 20, `backprop_depth=2`) + +### 1. Generate data + +```bash +# L=10 (14k train) +python generate_2arm_star.py --L 10 --n_train 14000 --n_valid 256 --seed 0 + +# L=15 (100k train) +python generate_2arm_star.py --L 15 --n_train 100000 --n_valid 256 --seed 0 + +# L=20 (100k train; rename to match the yaml paths) +python generate_2arm_star.py --L 20 --n_train 100000 --n_valid 256 --seed 0 +mv data/star_2arm_L20_train_fo_bfs.json data/star_2arm_L20_100k_train_fo_bfs.json +mv data/star_2arm_L20_valid_fo_bfs.json data/star_2arm_L20_100k_valid_fo_bfs.json +mv data/star_2arm_L20_test_fo_bfs.json data/star_2arm_L20_100k_test_fo_bfs.json +``` + +### 2. Stage-0 warm-starts + +```bash +# L10 stage-0 (cold) → ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nnodes 1 --nproc_per_node 1 \ + run.py args/star_coconut_L10_bfs_stage0.yaml + +# L15 stage-0 warm from L10 → ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nnodes 1 --nproc_per_node 1 \ + run.py args/star_coconut_L15_bfs_stage0_warm.yaml +``` + +L20 curriculum warm-starts from the same L15 stage-0 checkpoint. + +### 3. Train W=2 + backtracking + +| Depth | Config | Promote / BT gate | Warm-start | +|------:|--------|-------------------|------------| +| L=10 | `args/L10_w2_prom095_bt095.yaml` | CE @ 0.95 | L10 stage-0 | +| L=15 | `args/L15_w2_s1_prom095_bt095.yaml` | CE @ 0.95 | L15 stage-0 warm | +| L=20 | `args/L20_w2_s1_prom090_bt090.yaml` | CE @ 0.90 | L15 stage-0 warm | + +```bash +# L=10, backprop_depth=2 +CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nnodes 1 --nproc_per_node 1 \ + --master_port 29510 \ + run.py args/L10_w2_prom095_bt095.yaml + +# L=15, backprop_depth=2 +CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nnodes 1 --nproc_per_node 1 \ + --master_port 29515 \ + run.py args/L15_w2_s1_prom095_bt095.yaml + +# L=20, backprop_depth=2 +CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nnodes 1 --nproc_per_node 1 \ + --master_port 29520 \ + run.py args/L20_w2_s1_prom090_bt090.yaml +``` + +Checkpoints: `ckpts//`. Optional launchers: `scripts/launch_L15_w2_w5_s1.sh`, `scripts/launch_L20_bt_cso_pair.sh`. + +**L20 contrast (same CE@0.90 gate):** BT W=2 / BT W=5 finish the curriculum with high leaf accuracy; CSO (`args/L20_cso_prom090.yaml`) finishes the ladder but leaf accuracy stays near chance (~0.5). + +## Latent interventions (L=20) + +We probe finished L20 checkpoints by editing continuous thoughts, then measuring **leaf accuracy** on 128 val graphs. + +| Protocol | What we do | +|----------|------------| +| **Pin-last** | Keep the last thought intact; replace earlier thoughts with noise / other-graph donors | +| **Corrupt last** | Replace only the final thought | +| **Propagate** | Corrupt one mid-chain thought, then recompute all later thoughts | + +**Takeaway:** BT concentrates the answer in the **last** latent — wiping L1…L19 barely hurts if L20 is pinned; corrupting L20 (or propagating mid-chain noise) collapses accuracy toward chance. CSO is weak and flat under every edit. + +### Summary numbers (128 graphs) + +| Method | ckpt | clean | earlier→noise (last pinned) | last→donor | pin-last all-19 | +|--------|------|------:|----------------------------:|-----------:|----------------:| +| BT W=5 | `.../checkpoint_225` | 1.000 | 1.000 | 0.516 | 1.000 | +| BT W=2 | `.../checkpoint_225` | 0.930 | 0.922 | 0.430 | 0.930 | +| CSO | `.../checkpoint_200` | 0.531 | 0.516 | 0.531 | 0.531 | + +### Figures + +**All protocols (pin-last-k, aggregates, per-slot pin / propagate):** + +![L20 all intervention methods](figs/interventions/L20_all_methods.png) + +**Pin-last vs number of earlier latents corrupted:** + +![L20 pin-last k](figs/interventions/pinlast_k_L20.png) + +**Same pin-last-k as a table:** + +![L20 pin-last table](figs/interventions/pinlast_k_L20_table.png) + +**Earlier depths (L=10 / L=15) show the same BT last-thought concentration:** + +![L10 L15 pin-last](figs/interventions/pinlast_k_L10_L15.png) + +### Re-run interventions / regenerate plots + +```bash +# needs trained ckpts + val data on disk +python scripts/intervene_L20.py --ckpt ckpts/L20_w2_s1_prom090_bt090/checkpoint_225 --name "BT W=2" +python scripts/intervene_L20.py --ckpt ckpts/L20_w5_s1_prom090_bt090/checkpoint_225 --name "BT W=5" +python scripts/intervene_L20.py --ckpt ckpts/L20_cso_prom090/checkpoint_200 --name "CSO" + +# rebuild README figures from saved JSON (no GPU needed) +python scripts/plot_interventions_readme.py +``` + +Raw JSON: `figs/interventions/L20_*.json`, `figs/interventions/pinlast_k_*.json`. + +## Citation (base paper) + +```bibtex +@misc{zhu2025reasoning, + title = {Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought}, + author = {Hanlin Zhu and Shibo Hao and Zhiting Hu and Jiantao Jiao and Stuart Russell and Yuandong Tian}, + year = {2025}, + eprint = {2505.12514}, + archivePrefix = {arXiv}, + primaryClass = {cs.LG} +} +``` + +## License + +MIT — see LICENSE. diff --git a/args/L10_w1_prom098_bt098.yaml b/args/L10_w1_prom098_bt098.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3486116b8f37dae85688eb4f3b79ee174093ee85 --- /dev/null +++ b/args/L10_w1_prom098_bt098.yaml @@ -0,0 +1,58 @@ +# L10, W=1 for the whole curriculum after stage-0 warm start. +# Same recipe that solved L10 under full BPTT, but backprop_depth: 1 and +# both gates at ce_score 0.98 (stricter than the 0.95 winner). +project: coconut +save_path: ckpts +name: "L10_w1_prom098_bt098" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.98 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.98 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 +eval_print_full: False + +backprop_depth: 1 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L10_w1_prom099_bt099.yaml b/args/L10_w1_prom099_bt099.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2c2bc5389b6f8ce8d64746c51e6425c7dfcc29df --- /dev/null +++ b/args/L10_w1_prom099_bt099.yaml @@ -0,0 +1,56 @@ +# L10, W=1, both gates at ce_score 0.99. Same warm start as the L10 winner. +project: coconut +save_path: ckpts +name: "L10_w1_prom099_bt099" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.99 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.99 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 +eval_print_full: False + +backprop_depth: 1 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L10_w2_prom095_bt095.yaml b/args/L10_w2_prom095_bt095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e41ee1d0367e7a6b19d5f7744c53b7a5823d364c --- /dev/null +++ b/args/L10_w2_prom095_bt095.yaml @@ -0,0 +1,56 @@ +# L10 W=2 — same recipe as the L10 full-BPTT winner, backprop_depth: 2. +project: coconut +save_path: ckpts +name: "L10_w2_prom095_bt095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 +eval_print_full: False + +backprop_depth: 2 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L10_w2_prom098_bt098.yaml b/args/L10_w2_prom098_bt098.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e70e137b7308e6e08b94c11bfaf08514830d16bb --- /dev/null +++ b/args/L10_w2_prom098_bt098.yaml @@ -0,0 +1,56 @@ +# L10 W=2 with tighter ce@0.98 gates (ablation vs 0.95). +project: coconut +save_path: ckpts +name: "L10_w2_prom098_bt098" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.98 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.98 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 +eval_print_full: False + +backprop_depth: 2 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_2L_ce95_100k.yaml b/args/L15_push_2L_ce95_100k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1b5515985dfbbc553eb7229fee9fd1b904ff67bb --- /dev/null +++ b/args/L15_push_2L_ce95_100k.yaml @@ -0,0 +1,56 @@ +# Data lever: same as the mirror arm but all 100k graphs. +project: coconut +save_path: ckpts +name: "L15_push_2L_ce95_100k" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_2L_ce95_100k_w1.yaml b/args/L15_push_2L_ce95_100k_w1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a6f4e648516a00dfdfa3a721c25761454499ded4 --- /dev/null +++ b/args/L15_push_2L_ce95_100k_w1.yaml @@ -0,0 +1,59 @@ +# W=1 BPTT transfer test. +# Warm-start from 100k full-BPTT promote snapshot at stage 6 (hops 1..6 already +# meet ce_score@0.95). Remaining stages 6->15 train with backprop_depth: 1 so +# gradients only flow through the last latent recurrence step. +project: coconut +save_path: ckpts +name: "L15_push_2L_ce95_100k_w1" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 1 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_2L_ce95_100k_w1_prom098.yaml b/args/L15_push_2L_ce95_100k_w1_prom098.yaml new file mode 100644 index 0000000000000000000000000000000000000000..833662d87e96dc4a0c5e54f6726149bf85a6b7a3 --- /dev/null +++ b/args/L15_push_2L_ce95_100k_w1_prom098.yaml @@ -0,0 +1,59 @@ +# W=1 BPTT transfer, tighter PROMOTE gate (0.98). +# Same start as L15_push_2L_ce95_100k_w1: full-BPTT ckpt_120, init_stage 6, +# backprop_depth 1. Only change: promote_threshold 0.95 -> 0.98 so stages +# only advance when balance is very tight. Backtrack stays at 0.95. +project: coconut +save_path: ckpts +name: "L15_push_2L_ce95_100k_w1_prom098" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.98 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 1 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_2L_ce95_50k.yaml b/args/L15_push_2L_ce95_50k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7897235f45b80a7dfb8c295a57da1baacd431795 --- /dev/null +++ b/args/L15_push_2L_ce95_50k.yaml @@ -0,0 +1,56 @@ +# Exact L10-winner recipe at L15: 2-layer, ce_score@0.95 on both gates, fp32, 50k. +project: coconut +save_path: ckpts +name: "L15_push_2L_ce95_50k" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_4L_ce90_50k.yaml b/args/L15_push_4L_ce90_50k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..881661817afe73aff5ccff737854fdf322b6bcd7 --- /dev/null +++ b/args/L15_push_4L_ce90_50k.yaml @@ -0,0 +1,56 @@ +# Capacity + looser gate: 4-layer, ce_score@0.90, 50k. +project: coconut +save_path: ckpts +name: "L15_push_4L_ce90_50k" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.9 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.9 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-4layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_4L_ce95_50k.yaml b/args/L15_push_4L_ce95_50k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37d2dcb0520f5aa40ac8c94ae8259c6513b5de28 --- /dev/null +++ b/args/L15_push_4L_ce95_50k.yaml @@ -0,0 +1,56 @@ +# Capacity lever: 4-layer via layer-expansion warm start, ce_score@0.95, 50k. +project: coconut +save_path: ckpts +name: "L15_push_4L_ce95_50k" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-4layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_push_s0_4L.yaml b/args/L15_push_s0_4L.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f2b850fe5c381849ce29332b64af04dfbb9a2590 --- /dev/null +++ b/args/L15_push_s0_4L.yaml @@ -0,0 +1,56 @@ +# 4-layer L15 stage-0, pinned. Produces a native 4-layer stage-0 checkpoint in case layer-expansion warm start degrades hop-1. +project: coconut +save_path: ckpts +name: "L15_push_s0_4L" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: False +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-4layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w1_prom098_bt098.yaml b/args/L15_w1_prom098_bt098.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0dade9b0865dbb114b7055dfd2bb00a6fd31fcec --- /dev/null +++ b/args/L15_w1_prom098_bt098.yaml @@ -0,0 +1,56 @@ +# W=1 from stage-6 full-BPTT ckpt. BOTH gates at ce_score 0.98. +project: coconut +save_path: ckpts +name: "L15_w1_prom098_bt098" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.98 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.98 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 1 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w1_prom099_bt099.yaml b/args/L15_w1_prom099_bt099.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a027d78dfc3ed2aa41c64d210249712bc5b72268 --- /dev/null +++ b/args/L15_w1_prom099_bt099.yaml @@ -0,0 +1,56 @@ +# W=1 from stage-6 full-BPTT ckpt. BOTH gates at ce_score 0.99. +project: coconut +save_path: ckpts +name: "L15_w1_prom099_bt099" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.99 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.99 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 1 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w2_prom095_bt095.yaml b/args/L15_w2_prom095_bt095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cff604e8c070ace8f9659ca6bc066fea398dc039 --- /dev/null +++ b/args/L15_w2_prom095_bt095.yaml @@ -0,0 +1,57 @@ +# W=2 truncated BPTT from stage-6 full-BPTT ckpt. +# Same gates as the completed W=1@0.95 run for a clean speed/quality compare. +project: coconut +save_path: ckpts +name: "L15_w2_prom095_bt095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 2 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w2_s1_prom095_bt095.yaml b/args/L15_w2_s1_prom095_bt095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2cd2d5cdca7212b7fde6578abd7037855f98b7cc --- /dev/null +++ b/args/L15_w2_s1_prom095_bt095.yaml @@ -0,0 +1,57 @@ +# L15 W=2 from stage 1 (whole curriculum after stage0 warm-start). +# Same recipe as L15_push_2L_ce95_100k, backprop_depth: 2. +project: coconut +save_path: ckpts +name: "L15_w2_s1_prom095_bt095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 2 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w5_prom095_bt095.yaml b/args/L15_w5_prom095_bt095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b4d5ea50e16c2a6737a623c7e973c511a7194306 --- /dev/null +++ b/args/L15_w5_prom095_bt095.yaml @@ -0,0 +1,57 @@ +# W=5 truncated BPTT from stage-6 full-BPTT ckpt. +# Same gates as the completed W=1@0.95 run for a clean speed/quality compare. +project: coconut +save_path: ckpts +name: "L15_w5_prom095_bt095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 5 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w5_prom098_bt098.yaml b/args/L15_w5_prom098_bt098.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd06896a30b3518ece0dfff1321f40419a136ae0 --- /dev/null +++ b/args/L15_w5_prom098_bt098.yaml @@ -0,0 +1,56 @@ +# L15 W=5 from stage-6 full-BPTT ckpt, both gates at ce_score 0.98. +project: coconut +save_path: ckpts +name: "L15_w5_prom098_bt098" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 6 +promote_metric: ce_score +promote_threshold: 0.98 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.98 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 5 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/L15_push_2L_ce95_100k/checkpoint_120 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L15_w5_s1_prom095_bt095.yaml b/args/L15_w5_s1_prom095_bt095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5bfa1a2db4c14469932c0ebd2628ff67de7f7f0a --- /dev/null +++ b/args/L15_w5_s1_prom095_bt095.yaml @@ -0,0 +1,57 @@ +# L15 W=5 from stage 1 (whole curriculum after stage0 warm-start). +# Same recipe as L15_push_2L_ce95_100k, backprop_depth: 5. +project: coconut +save_path: ckpts +name: "L15_w5_s1_prom095_bt095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 5 +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L20_cso_prom090.yaml b/args/L20_cso_prom090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1dee252b61dd6e170dda29e5eb6afeb4f999a62d --- /dev/null +++ b/args/L20_cso_prom090.yaml @@ -0,0 +1,59 @@ +# L20 CSO (no backtracking) — fair contrast to L20_w2_s1_prom090_bt090. +# Same CE promote gate @ 0.90; promote_on_current_only so earlier hops can +# regress without blocking. No force-promote: if it stalls, that is the result +# (collapse / failure to finish curriculum). +project: coconut +save_path: ckpts +name: "L20_cso_prom090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 20 +promote_metric: ce_score +promote_threshold: 0.90 +promote_on_current_only: True +epochs_per_stage: 25 + +backtrack: False +backtrack_metric: ce_score +backtrack_detect_threshold: 0.90 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 5 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L20_w20_s1_prom090_bt090.yaml b/args/L20_w20_s1_prom090_bt090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..76323bda25ed0e065963b02a1b755377e0f6525b --- /dev/null +++ b/args/L20_w20_s1_prom090_bt090.yaml @@ -0,0 +1,58 @@ +# L20 BT — CE-gated from stage 1, FULL chain BPTT (W=null / all 20 latents). +# Same gates and warm-start as L20_w2_s1_prom090_bt090 / L20_w5_s1_prom090_bt090. +# backprop_depth: null => no truncated BPTT detach (coconut.py full BPTT). +project: coconut +save_path: ckpts +name: "L20_w20_s1_prom090_bt090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.90 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.90 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 5 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L20_w2_s1_prom090_bt090.yaml b/args/L20_w2_s1_prom090_bt090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d7d19fa2c7e703c6fe873d8c0d6347cc3796677d --- /dev/null +++ b/args/L20_w2_s1_prom090_bt090.yaml @@ -0,0 +1,58 @@ +# L20 BT — CE-gated from stage 1, W=2 throughout. +# Threshold lowered vs L15 (0.95 -> 0.90) so both BT and CSO share a fair, +# achievable gate at depth 20. Matched promote/backtrack on ce_score. +project: coconut +save_path: ckpts +name: "L20_w2_s1_prom090_bt090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.90 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.90 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 2 +train_size: 0 +save_only_improve: False +save_every: 5 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/L20_w5_s1_prom090_bt090.yaml b/args/L20_w5_s1_prom090_bt090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..57140df240e8e374de4f12aa0d9781904284cbdc --- /dev/null +++ b/args/L20_w5_s1_prom090_bt090.yaml @@ -0,0 +1,57 @@ +# L20 BT — CE-gated from stage 1, W=5 throughout. +# Same gates as L20_w2_s1_prom090_bt090 / L20_cso_prom090 (ce_score @ 0.90). +project: coconut +save_path: ckpts +name: "L20_w5_s1_prom090_bt090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 20 +promote_metric: ce_score +promote_threshold: 0.90 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.90 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: 5 +train_size: 0 +save_only_improve: False +save_every: 5 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_ce_thr050.yaml b/args/diag_L15_ce_thr050.yaml new file mode 100644 index 0000000000000000000000000000000000000000..258d886f77f49e4f8f4e6b277e14be4f4804acc8 --- /dev/null +++ b/args/diag_L15_ce_thr050.yaml @@ -0,0 +1,55 @@ +# L15 DIAGNOSTIC arm 3/4 — CE-score gated BT at threshold 0.50. +# 0.9/0.7 were unreachable at depth; 0.5 ≈ "both arms still have real mass" +# (~93/7 split for |F|=2) while still demanding superposition-ish balance. +project: coconut +save_path: ckpts +name: "diag-L15-ce-thr050" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.50 +epochs_per_stage: 25 +staging_metric: ce_score + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.50 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_ce_thr090.yaml b/args/diag_L15_ce_thr090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fb7e599ea8664af67f2afb89bc33689961dbf4b0 --- /dev/null +++ b/args/diag_L15_ce_thr090.yaml @@ -0,0 +1,53 @@ +# L15 DIAG — both-arms score (ce_score) gate at 0.90 + backtracking. +project: coconut +save_path: ckpts +name: "diag-L15-ce-thr090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.90 +epochs_per_stage: 25 +staging_metric: ce_score + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.90 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_ce_thr095.yaml b/args/diag_L15_ce_thr095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8735e906cbd79a0b8c3ffe574a4eb0c926faa8a3 --- /dev/null +++ b/args/diag_L15_ce_thr095.yaml @@ -0,0 +1,53 @@ +# L15 DIAG — both-arms score (ce_score) gate at 0.95 + backtracking. +project: coconut +save_path: ckpts +name: "diag-L15-ce-thr095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.95 +epochs_per_stage: 25 +staging_metric: ce_score + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.95 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_frontier_nobt_095.yaml b/args/diag_L15_frontier_nobt_095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eb7ceef32bd5e976de22e6fa845236660db02f90 --- /dev/null +++ b/args/diag_L15_frontier_nobt_095.yaml @@ -0,0 +1,54 @@ +# L15 DIAGNOSTIC arm 4/4 — frontier@0.95, NO backtracking, promote on current +# stage only. Isolates "how long does each stage take to hit 95%" without +# earlier-hop retention blocking promotion. +project: coconut +save_path: ckpts +name: "diag-L15-frontier-nobt-095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.95 +epochs_per_stage: 25 +staging_metric: frontier +promote_on_current_only: True + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: False +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_frontier_thr085.yaml b/args/diag_L15_frontier_thr085.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80ace7e8d1792931769a66b4c681bf362ce96036 --- /dev/null +++ b/args/diag_L15_frontier_thr085.yaml @@ -0,0 +1,53 @@ +# L15 DIAGNOSTIC arm 2/4 — frontier-gated BT at threshold 0.85 (lower bar). +project: coconut +save_path: ckpts +name: "diag-L15-frontier-thr085" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.85 +epochs_per_stage: 25 +staging_metric: frontier + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.85 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_frontier_thr090.yaml b/args/diag_L15_frontier_thr090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..582c113ff01b4dee036a57e19604a25c73d17f3d --- /dev/null +++ b/args/diag_L15_frontier_thr090.yaml @@ -0,0 +1,53 @@ +# L15 DIAG — hop-accuracy (frontier) gate at 0.90 + backtracking. +project: coconut +save_path: ckpts +name: "diag-L15-frontier-thr090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.90 +epochs_per_stage: 25 +staging_metric: frontier + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.90 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_frontier_thr095.yaml b/args/diag_L15_frontier_thr095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8739999b444014f92d4f7f3d62cbd461a1df4ba6 --- /dev/null +++ b/args/diag_L15_frontier_thr095.yaml @@ -0,0 +1,55 @@ +# L15 DIAGNOSTIC arm 1/4 — frontier-gated BT at promote/BT threshold 0.95. +# Start from stage 0 (warm hop-1 ckpt). Measure epochs-to-0.95 per stage. +# ce_score is always logged alongside frontier for BT-metric diagnosis. +project: coconut +save_path: ckpts +name: "diag-L15-frontier-thr095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.95 +epochs_per_stage: 25 +staging_metric: frontier + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.95 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_frontier_thr099.yaml b/args/diag_L15_frontier_thr099.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4cbd6a9d36579549bb27d495d955a3ed1192948f --- /dev/null +++ b/args/diag_L15_frontier_thr099.yaml @@ -0,0 +1,55 @@ +# L15 DIAG — hop-accuracy gate at 0.99 + backtracking. +# Hypothesis: 0.95 promotes too fast (~2 epochs/stage early on); 0.99 forces +# each latent stage to be nearly solved before advancing. +project: coconut +save_path: ckpts +name: "diag-L15-frontier-thr099" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.99 +epochs_per_stage: 25 +staging_metric: frontier + +eval_every: 2 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.99 +revert_staging: False + +train_size: 50000 +save_only_improve: False +save_every: 50 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promCE090_btCE090.yaml b/args/diag_L15_promCE090_btCE090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7087d19301329212897769851e410a942dc2b412 --- /dev/null +++ b/args/diag_L15_promCE090_btCE090.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): ce_score >= 0.9 +# BACKTRACK (retrain earlier): ce_score < 0.9 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promCE090_btCE090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: ce_score +promote_threshold: 0.9 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.9 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF095_btCE050.yaml b/args/diag_L15_promF095_btCE050.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ffc2c318c64cab56856f208a46a3d11cfaaa216 --- /dev/null +++ b/args/diag_L15_promF095_btCE050.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.95 +# BACKTRACK (retrain earlier): ce_score < 0.5 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promF095_btCE050" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.5 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF095_btCE090.yaml b/args/diag_L15_promF095_btCE090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..da943fc439b213ea419d979780feaec94bd43341 --- /dev/null +++ b/args/diag_L15_promF095_btCE090.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.95 +# BACKTRACK (retrain earlier): ce_score < 0.9 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promF095_btCE090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.9 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF095_btCE095.yaml b/args/diag_L15_promF095_btCE095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..effbfeb3b7e6f452f917f9e0fd248fbe4cbae02f --- /dev/null +++ b/args/diag_L15_promF095_btCE095.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.95 +# BACKTRACK (retrain earlier): ce_score < 0.95 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promF095_btCE095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF095_btF095.yaml b/args/diag_L15_promF095_btF095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..94c8f5d349f640fd9b8a0cb5a6c607f3cbcd42ec --- /dev/null +++ b/args/diag_L15_promF095_btF095.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.95 +# BACKTRACK (retrain earlier): frontier < 0.95 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promF095_btF095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: frontier +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF095_btNONE.yaml b/args/diag_L15_promF095_btNONE.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3adaa0dd684b2dd1cb815ef52a3c79a6e9285c8c --- /dev/null +++ b/args/diag_L15_promF095_btNONE.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.95 +# BACKTRACK (retrain earlier): OFF +project: coconut +save_path: ckpts +name: "diag_L15_promF095_btNONE" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.95 +promote_on_current_only: True +epochs_per_stage: 25 + +backtrack: False +backtrack_metric: frontier +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF099_btCE050.yaml b/args/diag_L15_promF099_btCE050.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2401fb2bb1bd819cf65d56e007e2e3df6ca5cd5c --- /dev/null +++ b/args/diag_L15_promF099_btCE050.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.99 +# BACKTRACK (retrain earlier): ce_score < 0.5 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promF099_btCE050" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.99 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.5 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_L15_promF099_btCE090.yaml b/args/diag_L15_promF099_btCE090.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e9a4fe737837cf5a60d20faadf389b8b5d63115 --- /dev/null +++ b/args/diag_L15_promF099_btCE090.yaml @@ -0,0 +1,58 @@ +# L15 DIAG — SEPARATE gates +# PROMOTE (stage i -> i+1): frontier >= 0.99 +# BACKTRACK (retrain earlier): ce_score < 0.9 triggers retrain +project: coconut +save_path: ckpts +name: "diag_L15_promF099_btCE090" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: frontier +promote_threshold: 0.99 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.9 +remember_rate: 0.3 +revert_staging: False + +eval_every: 10 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 50000 +save_only_improve: False +save_every: 200 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_99 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/diag_ctrl.yaml b/args/diag_ctrl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..07cef435ee5b2a59842e9d7a16541d7c4e77ad56 --- /dev/null +++ b/args/diag_ctrl.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-cc-d1-14k-ctrl" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: False diff --git a/args/diag_lr1e3.yaml b/args/diag_lr1e3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f299679c61ffa5113372c711c04edc5ec66bd4d --- /dev/null +++ b/args/diag_lr1e3.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-14k-lr1e3" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: !!float "1e-3" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/diag_lr3e4.yaml b/args/diag_lr3e4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9356ce6d9b93e3df9615de81e6c72bcda956d63 --- /dev/null +++ b/args/diag_lr3e4.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-14k-lr3e4" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: !!float "3e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/diag_small.yaml b/args/diag_small.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6993b923f18556ce665ae23901b58cac78e5ddde --- /dev/null +++ b/args/diag_small.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-14k-small" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-128dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 40 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/diag_smallL.yaml b/args/diag_smallL.yaml new file mode 100644 index 0000000000000000000000000000000000000000..93a225c9ee97ca7196cfee7b39cd01a44eddb594 --- /dev/null +++ b/args/diag_smallL.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-14k-smallL" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-128dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/diag_stage0_bs256.yaml b/args/diag_stage0_bs256.yaml new file mode 100644 index 0000000000000000000000000000000000000000..62d5e6adbbce13105d282972a365e00ed5aaf365 --- /dev/null +++ b/args/diag_stage0_bs256.yaml @@ -0,0 +1,42 @@ +project: coconut +save_path: ckpts +name: diag_stage0_bs256 +only_eval: false +coconut: true +cot: false +no_thoughts: false +no_cot: false +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: true +loss_staging: true +init_stage: 0 +promote_loss_threshold: 1.5 +epochs_per_stage: 25 +log_every: 5 +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 +backprop_depth: null +backtrack: false +accuracy_staging: false +save_only_improve: false +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: false +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: false +batch_size_training: 256 +debug: false +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: 0.0001 +grad_clip: 1.0 +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: true +save_every: 40 diff --git a/args/diag_stage0_ws2_bs128.yaml b/args/diag_stage0_ws2_bs128.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa332c87d16d6bb94fb6ef80c582b3100d9d4ac4 --- /dev/null +++ b/args/diag_stage0_ws2_bs128.yaml @@ -0,0 +1,42 @@ +project: coconut +save_path: ckpts +name: diag_stage0_ws2_bs128 +only_eval: false +coconut: true +cot: false +no_thoughts: false +no_cot: false +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: true +loss_staging: true +init_stage: 0 +promote_loss_threshold: 1.5 +epochs_per_stage: 25 +log_every: 5 +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 +backprop_depth: null +backtrack: false +accuracy_staging: false +save_only_improve: false +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: false +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: false +batch_size_training: 128 +debug: false +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: 0.0001 +grad_clip: 1.0 +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: true +save_every: 40 diff --git a/args/diag_verify_L10_w2_from_stage0.yaml b/args/diag_verify_L10_w2_from_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..54d202b3c7f9b0a0aff0a3a6632a25bcdc1ac4e8 --- /dev/null +++ b/args/diag_verify_L10_w2_from_stage0.yaml @@ -0,0 +1,49 @@ +accuracy_staging: true +backprop_depth: 2 +backtrack: true +backtrack_detect_threshold: 0.95 +backtrack_metric: ce_score +batch_size_training: 128 +bf16: false +bfs_variant: true +c_thought: 1 +coconut: true +cot: false +debug: false +epochs_per_stage: 25 +eval_every: 5 +eval_print_full: false +grad_clip: 1.0 +gradient_accumulation_steps: 1 +init_stage: 1 +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +log_every: 5 +lr: 0.0001 +max_latent_stage: 10 +model_id: configs/symbol-2layer-8head-768dim-L20.json +name: diag_verify_L10_w2_from_stage0 +no_cot: false +no_thoughts: false +num_epochs: 25 +only_eval: false +pad_latent_to_max: true +perhop_train_samples: 128 +perhop_val_samples: 256 +project: coconut +promote_metric: ce_score +promote_on_current_only: false +promote_threshold: 0.95 +remember_rate: 0.3 +reset_optimizer: false +resume: 0 +revert_staging: false +save_every: 25 +save_only_improve: false +save_path: ckpts +seed: 0 +train_path: data/star_2arm_L10_train_fo_bfs.json +train_size: 0 +uniform_prob: 0.1 +val_path: data/star_2arm_L10_valid_fo_bfs.json +warmup_steps: 200 +weight_decay: 0.01 diff --git a/args/diag_wd.yaml b/args/diag_wd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8b26e228c668a9a36c699815acb617d3f4c0aa76 --- /dev/null +++ b/args/diag_wd.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-14k-wd" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: !!float "1e-4" +weight_decay: 0.1 +bfs_variant: False +final_only: True diff --git a/args/eval_prosqa_coconut_2l_8h_768d.yaml b/args/eval_prosqa_coconut_2l_8h_768d.yaml new file mode 100644 index 0000000000000000000000000000000000000000..811c827ba8a57fa0d56e4817f7e40c75b826ad6f --- /dev/null +++ b/args/eval_prosqa_coconut_2l_8h_768d.yaml @@ -0,0 +1,34 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "eval-prosqa-coconut-2l-8h-768d" + +only_eval: True + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: PATH_TO_YOUR_CHECKPOINT +seed: 0 +resume: 299 +bf16: False +train_path: data/prosqa_train_graph_4_coconut.json +val_path: data/prosqa_test_graph_4_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 \ No newline at end of file diff --git a/args/final_L15_ce_bt_full.yaml b/args/final_L15_ce_bt_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ee38f26fc6b2ee00ee3fac4341d0ae6d4f45642e --- /dev/null +++ b/args/final_L15_ce_bt_full.yaml @@ -0,0 +1,61 @@ +# L15 ARM 3/3 — BT gated on the cross-entropy score, FULL BPTT. +# +# staging_metric: ce_score is the loss-based gate: exp(log|F| - CE) against +# Uniform(F), which is 1.0 only when BOTH arms of the frontier hold comparable +# probability mass. Unlike `frontier` (argmax lands anywhere on the frontier) or +# `superposition` (top-|F| set equality, a rank-only test), it fails a model that has +# collapsed one arm to near-zero probability -- so it is the strictest of the three. +project: coconut +save_path: ckpts +name: "final-L15-ce-bt-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 14 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: ce_score + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L15_ce_bt_full_thr07.yaml b/args/final_L15_ce_bt_full_thr07.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38a809b1ef34ac63af67863103b3793f450ea72c --- /dev/null +++ b/args/final_L15_ce_bt_full_thr07.yaml @@ -0,0 +1,67 @@ +# L15 ARM 4/4 — BT gated on ce_score, but at threshold 0.70 instead of 0.90. +# +# Why a second CE arm: ce_score = exp(log|F| - CE) with CE against Uniform(F), so for +# |F|=2 the threshold translates directly into a demanded probability split -- +# ce_score 0.90 <=> ~72/28 or better, on average, at EVERY hop +# ce_score 0.80 <=> ~80/20 +# ce_score 0.70 <=> ~86/14 +# 0.90 is therefore far stricter than "both arms are emittable"; it demands near-parity. +# The 0.90 arms confirm this empirically: within 20 epochs the L15 one backtracked all +# the way to stage 2 and the L20 one to stage 0, because deep hops sit near ce_score +# 0.12-0.19 and can never clear the bar. 0.70 still rejects a collapsed arm (14% mass +# is comfortably emittable) while being reachable, so this arm tells us whether the +# CE gate fails because CE is the wrong signal or merely because 0.90 was miscalibrated. +project: coconut +save_path: ckpts +name: "final-L15-ce-bt-thr07" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 14 +promote_threshold: 0.7 +epochs_per_stage: 25 + +staging_metric: ce_score + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.7 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L15_frontier_bt_full.yaml b/args/final_L15_frontier_bt_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4dec33fb1e82ae92f8f3b0988a243cdcd9e477de --- /dev/null +++ b/args/final_L15_frontier_bt_full.yaml @@ -0,0 +1,62 @@ +# L15 ARM 1/3 — frontier-gated BT, FULL BPTT. Reference arm for the W=1 ablation. +# +# Resumes the previous run at its blocked stage: it reached stage 14 and then sat +# 1220 epochs with hop-15 frontier stuck at 0.844 (bar 0.9). Two changes vs before: +# train_size 30000 -> 50000 (the plateau shape matches a data limit, not an +# optimization-time limit: 1220 epochs bought nothing at 30k) +# num_epochs 2000 -> 5000 +# Pair with final_L15_frontier_bt_w1.yaml (identical except backprop_depth: 1). +project: coconut +save_path: ckpts +name: "final-L15-frontier-bt-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 14 # resume at the blocked stage; BT can still pull back +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null # FULL BPTT + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L15_frontier_bt_w1.yaml b/args/final_L15_frontier_bt_w1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80d53c8322730894a3a54025a6b4df65bca9cf1f --- /dev/null +++ b/args/final_L15_frontier_bt_w1.yaml @@ -0,0 +1,62 @@ +# L15 ARM 2/3 — frontier-gated BT, TRUNCATED BPTT W=1. +# +# Byte-identical to final_L15_frontier_bt_full.yaml except backprop_depth: 1, so the +# pair isolates the effect of only back-propagating through the LAST latent step. +# Rationale for expecting this to be harmless: the earlier latent depths are not +# relying on long-range credit assignment for their signal -- each depth is supervised +# directly by its own curriculum stage, plus uniform_prob=0.1 rehearsal and the +# backtracking passes. Measured cost saving at depth 15 is 567ms -> 457ms/step (19%). +project: coconut +save_path: ckpts +name: "final-L15-frontier-bt-w1" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 14 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: 1 # gradient only through the final latent step + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L20_ce_bt_full.yaml b/args/final_L20_ce_bt_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c927a82f0bef0e2c8eca82266f61ccb9fe41a910 --- /dev/null +++ b/args/final_L20_ce_bt_full.yaml @@ -0,0 +1,59 @@ +# L20 ARM 3/4 — BT gated on the cross-entropy score, FULL BPTT, 50k graphs. +# +# The loss-gated arm at depth 20: ce_score = exp(log|F| - CE) against Uniform(F), +# so a hop only promotes once both frontier arms carry real probability mass, not +# merely once the argmax lands somewhere on the frontier. +project: coconut +save_path: ckpts +name: "final-L20-ce-bt-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: ce_score + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L20-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L20_frontier_bt_full.yaml b/args/final_L20_frontier_bt_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..220262b7f6e64c4bf399678d7144f71c7236b90d --- /dev/null +++ b/args/final_L20_frontier_bt_full.yaml @@ -0,0 +1,61 @@ +# L20 ARM 1/4 — frontier-gated BT, FULL BPTT, 50k graphs. +# +# The previous L20 attempt cleared hop-1 (0.945) then sat 1880 epochs at stage 1 with +# hop-2 frontier stuck at 0.855. Same plateau signature as every earlier failure that +# turned out to be sample complexity rather than capacity (L12 needed 50k, not 14k), +# so the lever here is train_size 30000 -> 50000 while resuming the hop-1 solution. +# Reference arm for the L20 W=1 ablation. +project: coconut +save_path: ckpts +name: "final-L20-frontier-bt-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 # hop-1 already solved by the loaded checkpoint +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null # FULL BPTT + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L20-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L20_frontier_bt_w1.yaml b/args/final_L20_frontier_bt_w1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7d42cb15b77d9dd78d25802edbf4301ae3a5bc7f --- /dev/null +++ b/args/final_L20_frontier_bt_w1.yaml @@ -0,0 +1,61 @@ +# L20 ARM 2/4 — frontier-gated BT, TRUNCATED BPTT W=1, 50k graphs. +# +# Identical to final_L20_frontier_bt_full.yaml except backprop_depth: 1. This is the +# arm that tests the "only the last latent step matters" hypothesis at the depth where +# it should pay off most: at 20 latents, full BPTT costs 684ms/step vs 536ms at W=1 +# (22% saving), and W=1 also removes 19 steps of gradient path that could be diluting +# the signal for the one hop actually being supervised. +project: coconut +save_path: ckpts +name: "final-L20-frontier-bt-w1" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: 1 # gradient only through the final latent step + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 50000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L20-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/final_L20_frontier_bt_w1_100k.yaml b/args/final_L20_frontier_bt_w1_100k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e69aeb0017e714f26bca08c7579d60097760873 --- /dev/null +++ b/args/final_L20_frontier_bt_w1_100k.yaml @@ -0,0 +1,60 @@ +# L20 ARM 4/4 — frontier-gated BT, W=1, FULL 100k graphs. +# +# The "throw everything at depth 20" arm, and the data ablation against +# final_L20_frontier_bt_w1.yaml (same in every respect but 50k -> 100k). W=1 is what +# buys back some of the 2x epoch-time cost of doubling the data. If 50k still plateaus +# at hop 2 the way 30k did, this arm says whether the ceiling is data at all. +project: coconut +save_path: ckpts +name: "final-L20-frontier-bt-w1-100k" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: 1 + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 # 0 = use the whole file (100k) + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L20-bfs-backtrack-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 5000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/prosqa_coconut_2l_8h_768d.yaml b/args/prosqa_coconut_2l_8h_768d.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5a3bd4f15715c2c503ece10f1dae23ab72a74575 --- /dev/null +++ b/args/prosqa_coconut_2l_8h_768d.yaml @@ -0,0 +1,34 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "prosqa-coconut-2l-8h-768d" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/prosqa_train_graph_4_coconut.json +val_path: data/prosqa_valid_graph_4_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 \ No newline at end of file diff --git a/args/prosqa_coconut_bfs.yaml b/args/prosqa_coconut_bfs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7d903a12ab8039879ccdf32077069d429276c608 --- /dev/null +++ b/args/prosqa_coconut_bfs.yaml @@ -0,0 +1,35 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "coconut-2l-prosqa-bfs" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/prosqa_train_graph_4_bfs.json +val_path: data/prosqa_valid_graph_4_bfs_shuffled.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: True diff --git a/args/prosqa_coconut_full.yaml b/args/prosqa_coconut_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..50a37623101e9beee3580bd395379049dc033a5a --- /dev/null +++ b/args/prosqa_coconut_full.yaml @@ -0,0 +1,35 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "coconut-2l-prosqa-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/prosqa_train_graph_4_coconut.json +val_path: data/prosqa_valid_graph_4_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False diff --git a/args/prosqa_coconut_revert.yaml b/args/prosqa_coconut_revert.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18084a071363797278118579b1fa46248d47816c --- /dev/null +++ b/args/prosqa_coconut_revert.yaml @@ -0,0 +1,35 @@ +project: coconut +save_path: ckpts +name: "coconut-2l-prosqa-revert" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/prosqa_train_graph_4_coconut.json +val_path: data/prosqa_valid_graph_4_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +revert_staging: True +revert_threshold: 0.8 +revert_metric: frontier diff --git a/args/prosqa_cot.yaml b/args/prosqa_cot.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f1f5d37cf2e75ae69d61ece40ad8fbfde6b40f6b --- /dev/null +++ b/args/prosqa_cot.yaml @@ -0,0 +1,34 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "cot-2l-prosqa" + +only_eval: False + +coconut: False +cot: True +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/prosqa_train_graph_4_coconut.json +val_path: data/prosqa_valid_graph_4_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 \ No newline at end of file diff --git a/args/prosqa_nocot.yaml b/args/prosqa_nocot.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3569d8b5daee82663230a70e670f52901cca27aa --- /dev/null +++ b/args/prosqa_nocot.yaml @@ -0,0 +1,34 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "nocot-2l-prosqa" + +only_eval: False + +coconut: False +cot: False +no_thoughts: False +no_cot: True + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 4 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/prosqa_train_graph_4_coconut.json +val_path: data/prosqa_valid_graph_4_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 \ No newline at end of file diff --git a/args/star_B4_L10_ce95.yaml b/args/star_B4_L10_ce95.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e3f5253421829b1903087bb5ba6a9f92841da47d --- /dev/null +++ b/args/star_B4_L10_ce95.yaml @@ -0,0 +1,68 @@ +# B=4 branch, depth 10. First width experiment: does the model hold a FOUR-way +# superposition, or quietly collapse to representing 2 of the 4 branches? +# +# The frontier at every hop is 4 nodes, so ce_score = 4 * GM(p over F) and the +# 0.95 gate demands all four branches stay tightly near p = 0.25. Hop accuracy +# would be blind to a 2-of-4 collapse; the balance score is not. +# +# Same recipe that solved L15 at B=2: ce_score on BOTH promote and backtrack at +# 0.95, fp32, full BPTT, 100k graphs, 2-layer/768 model. +# +# Warm start from the B=2 L10 stage-0 checkpoint: same vocab and model, and the +# edge-lookup circuit transfers even though that model only ever emitted a +# 2-way frontier. Cold stage-0 memorised at this prompt length before. +project: coconut +save_path: ckpts +name: "star_B4_L10_ce95" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_4arm_L10_train_fo_bfs.json +val_path: data/star_4arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_B4_L10_ce95_250k.yaml b/args/star_B4_L10_ce95_250k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..581e89ab8f8e8a6fb6ba76c7de1c468083b4bb71 --- /dev/null +++ b/args/star_B4_L10_ce95_250k.yaml @@ -0,0 +1,57 @@ +# B=4 L=10 with 250k graphs (was stuck ~0.78 hop1 ce on 100k). +# Same 2-layer model + ce@0.95 both gates + full BPTT. +project: coconut +save_path: ckpts +name: "star_B4_L10_ce95_250k" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_4arm250k_L10_train_fo_bfs.json +val_path: data/star_4arm250k_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_B4_L10_ce95_250k_4L.yaml b/args/star_B4_L10_ce95_250k_4L.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fea1afb6c69a2bb99150d62409e68a24d05b6cbc --- /dev/null +++ b/args/star_B4_L10_ce95_250k_4L.yaml @@ -0,0 +1,57 @@ +# B=4 L=10, 250k data, 4-layer capacity test. +# Layer-expands from the 2-layer L10 stage-0 ckpt (h.2/h.3 random). +project: coconut +save_path: ckpts +name: "star_B4_L10_ce95_250k_4L" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_metric: ce_score +promote_threshold: 0.95 +promote_on_current_only: False +epochs_per_stage: 25 + +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False + +eval_every: 5 +log_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 25 +uniform_prob: 0.1 +model_id: configs/symbol-4layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_4arm250k_L10_train_fo_bfs.json +val_path: data/star_4arm250k_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 4000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_accstage_nobt.yaml b/args/star_coconut_L10_bfs_accstage_nobt.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eef4eae0b1f6d4eb6b6f16e8aa941637c90ed7c1 --- /dev/null +++ b/args/star_coconut_L10_bfs_accstage_nobt.yaml @@ -0,0 +1,58 @@ +# Depth-10 2-arm star, latent CoT (coconut), ACCURACY-GATED curriculum WITHOUT +# backtracking/rehearsal. Experiment arm A (the control). Compare against +# star_coconut_L10_bfs_accstage_nobt.yaml (arm A, identical but backtrack: True). +# Both save best-accuracy checkpoints for the later latent-interpretability work. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-accstage-nobt" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 # CoT warmup at stage 0, then promote 0->1->...->10 +promote_threshold: 0.9 # advance only when min frontier acc over 1..cur_stage >= this +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +eval_every: 5 # per-hop eval drives promotion; keep responsive +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null # full BPTT (cheap for 2-layer/768d) + +# ---- backtracking / rehearsal ---- +backtrack: False +remember_rate: 0.3 # prob of broad rehearsal; else targeted [r_current..frontier] +backtrack_detect_threshold: 0.9 # per-hop acc below which a stage is "regressed" +revert_staging: False # soft rehearsal, not hard revert + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 # budget cap; accuracy-gating decides real length +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_backtrack.yaml b/args/star_coconut_L10_bfs_backtrack.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3c705e6fa204acbcbbc7ad366340d30098dfb9be --- /dev/null +++ b/args/star_coconut_L10_bfs_backtrack.yaml @@ -0,0 +1,58 @@ +# Depth-10 2-arm star, latent CoT (coconut), ACCURACY-GATED curriculum WITH +# backtracking/rehearsal. Experiment arm B (the treatment). Compare against +# star_coconut_L10_bfs_accstage_nobt.yaml (arm A, identical but backtrack: False). +# Both save best-accuracy checkpoints for the later latent-interpretability work. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-backtrack" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 # CoT warmup at stage 0, then promote 0->1->...->10 +promote_threshold: 0.9 # advance only when min frontier acc over 1..cur_stage >= this +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +eval_every: 5 # per-hop eval drives promotion; keep responsive +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null # full BPTT (cheap for 2-layer/768d) + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 # prob of broad rehearsal; else targeted [r_current..frontier] +backtrack_detect_threshold: 0.9 # per-hop acc below which a stage is "regressed" +revert_staging: False # soft rehearsal, not hard revert + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 # budget cap; accuracy-gating decides real length +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_backtrack_ce.yaml b/args/star_coconut_L10_bfs_backtrack_ce.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8f430b48a34a7a99fb85bac9352afd67d9747766 --- /dev/null +++ b/args/star_coconut_L10_bfs_backtrack_ce.yaml @@ -0,0 +1,73 @@ +# Depth-10 2-arm star, accuracy-gated curriculum WITH backtracking, gated on +# CE_SCORE: exp(log|F| - CE) where CE is the cross-entropy against Uniform(F). +# +# This is the graded counterpart of `superposition`. That metric is a rank +# statistic (top-|F| logits == frontier set), so it scores p=(0.98, 0.001) +# identically to p=(0.5, 0.5) and cannot tell a real two-arm superposition from +# a model that committed to one arm and merely ranks the other second. ce_score +# reads the actual probabilities via the geometric mean over F, which collapses +# if either arm collapses. 1.0 == exactly at the log|F| floor. +# +# Threshold stays 0.9, calibrated on existing L10 checkpoints so the comparison +# against the other three arms uses an identical bar: +# superposition-gated ckpt_400 (healthy): ce_score 0.932-0.963 per hop -> passes +# frontier-gated ckpt_650 (decayed tail): ce_score 0.887 -> 0.357 -> flagged +# Note the frontier-gated model is flagged from hop 1, where the old metric still +# read 1.000; ce_score sees the imbalance ~5 hops before the rank statistic does. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-backtrack-ce" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 1 # skip stage 0; load a hop-1-mastered checkpoint below +promote_threshold: 0.9 +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +staging_metric: ce_score # graded superposition; frontier/optimal/superposition still logged + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 # same threshold, but scored by staging_metric +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +# Same warm start as the superposition arm, so the two are directly comparable. +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_backtrack_ce095.yaml b/args/star_coconut_L10_bfs_backtrack_ce095.yaml new file mode 100644 index 0000000000000000000000000000000000000000..67ecc601ba7b19434a11ad4fb292c9124953ce90 --- /dev/null +++ b/args/star_coconut_L10_bfs_backtrack_ce095.yaml @@ -0,0 +1,55 @@ +# L10 BT — same as successful ce@0.9 arm, but stricter both-arms bar: 0.95. +# Promote + backtrack both use ce_score (matched, like the original L10 CE run). +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-backtrack-ce095" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.95 +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.95 +remember_rate: 0.3 +revert_staging: False +epochs_per_stage: 25 + +eval_every: 5 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 128 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 100 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_backtrack_ce099.yaml b/args/star_coconut_L10_bfs_backtrack_ce099.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0edea6622a53f95fb03873a160353c3d35029f71 --- /dev/null +++ b/args/star_coconut_L10_bfs_backtrack_ce099.yaml @@ -0,0 +1,54 @@ +# L10 BT — same as successful ce@0.9 arm, but stricter both-arms bar: 0.99. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-backtrack-ce099" + +only_eval: False +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_metric: ce_score +promote_threshold: 0.99 +backtrack: True +backtrack_metric: ce_score +backtrack_detect_threshold: 0.99 +remember_rate: 0.3 +revert_staging: False +epochs_per_stage: 25 + +eval_every: 5 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 128 +eval_print_full: False + +backprop_depth: null +train_size: 0 +save_only_improve: False +save_every: 100 +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_backtrack_superpos.yaml b/args/star_coconut_L10_bfs_backtrack_superpos.yaml new file mode 100644 index 0000000000000000000000000000000000000000..185b583f65398b0fe788c04fc71fead5389c68c4 --- /dev/null +++ b/args/star_coconut_L10_bfs_backtrack_superpos.yaml @@ -0,0 +1,66 @@ +# Depth-10 2-arm star, accuracy-gated curriculum WITH backtracking, using +# SUPERPOSITION accuracy (top-|F| == reachable frontier set) as the promote / +# backtrack metric. Starts from a mastered stage-0 checkpoint so we skip hop-1 +# warmup and exercise the new metric from stage 1 upward. +# +# Default frontier-accuracy gating is unchanged: omit `staging_metric` (or set +# it to "frontier") to recover the previous behaviour. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-backtrack-superpos" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 1 # skip stage 0; load a hop-1-mastered checkpoint below +promote_threshold: 0.9 +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +# Optional staging metric. "superposition" = top-|F| logits equal the reachable +# frontier set (both arms). Omit / "frontier" / "optimal" keep prior behaviour. +staging_metric: superposition + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 # same threshold, but scored by staging_metric +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +# Hop-1 frontier = 1.0 at this ckpt (stage-0-pinned run, epoch 150). +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_curstage.yaml b/args/star_coconut_L10_bfs_curstage.yaml new file mode 100644 index 0000000000000000000000000000000000000000..157613838865df21795e24d0ab4ef23aa9974b98 --- /dev/null +++ b/args/star_coconut_L10_bfs_curstage.yaml @@ -0,0 +1,62 @@ +# Depth-10 2-arm star, latent CoT (coconut), ACCURACY-GATED curriculum WITHOUT +# backtracking/rehearsal. Experiment current-stage-only promotion (fair no-backtrack baseline). Compare against +# star_coconut_L10_bfs_accstage_nobt.yaml (arm A, identical but backtrack: True). +# Both save best-accuracy checkpoints for the later latent-interpretability work. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-curstage" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 # CoT warmup at stage 0, then promote 0->1->...->10 +promote_threshold: 0.9 # advance only when min frontier acc over 1..cur_stage >= this +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +eval_every: 5 # per-hop eval drives promotion; keep responsive +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null # full BPTT (cheap for 2-layer/768d) + +# ---- backtracking / rehearsal ---- +backtrack: False +remember_rate: 0.3 # prob of broad rehearsal; else targeted [r_current..frontier] +backtrack_detect_threshold: 0.9 # per-hop acc below which a stage is "regressed" +revert_staging: False # soft rehearsal, not hard revert + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 # budget cap; accuracy-gating decides real length +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True + +# Fair no-backtracking baseline: promote when ONLY the current stage hop is +# solved (no earlier-stage retention requirement, no repair). +promote_on_current_only: True diff --git a/args/star_coconut_L10_bfs_full.yaml b/args/star_coconut_L10_bfs_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ede3a501ca14f59cd6a77379056b565fa30b09e3 --- /dev/null +++ b/args/star_coconut_L10_bfs_full.yaml @@ -0,0 +1,56 @@ +# FULL curriculum at depth 10, but promotion is LOSS-GATED instead of a fixed +# 25-epoch clock. The fixed schedule promoted off stage 0 at epoch 25 while its +# eval CE was still ~3.9 (L10 stage 0 needs ~60 epochs to converge to ~0.7), +# which cascaded into 0% accuracy at every later stage. Here we hold each stage +# until its eval CE <= promote_loss_threshold, then advance up to stage 10. +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 10 +pad_latent_to_max: True + +# Loss-gated staging: advance only when current-stage eval CE is low enough. +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.0 +epochs_per_stage: 25 # unused fallback under loss_staging + +# Check promotion (cheap val CE) every 5 epochs; expensive per-hop acc every 25. +log_every: 5 +eval_every: 25 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +# Large budget; loss-gating decides real length. Monitor and kill when done. +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_plain.yaml b/args/star_coconut_L10_bfs_plain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..634cc5a97f7cac941e41034bb56f83323ebd00aa --- /dev/null +++ b/args/star_coconut_L10_bfs_plain.yaml @@ -0,0 +1,36 @@ +# DIAGNOSTIC: exact L6-v128 recipe at depth 10 only. No accuracy-staging, no +# backtracking, full BPTT. Bisects L6 (works) vs L20 (stalls at stage 0). +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-plain" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 10 +pad_latent_to_max: True +eval_every: 25 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L10_bfs_stage0.yaml b/args/star_coconut_L10_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..123b0d47fdc1d18a59dc707dee8af27de4056513 --- /dev/null +++ b/args/star_coconut_L10_bfs_stage0.yaml @@ -0,0 +1,53 @@ +# DIAGNOSTIC: depth-10, PINNED at stage 0. Loss-gated curriculum is ON, but +# max_latent_stage == init_stage == 0 so we never promote — just watch whether +# stage-0 eval CE decreases (same hop-1 neighbor target as L6 stage 0). +project: coconut +save_path: ckpts +name: "star-coconut-L10-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +# Loss-gated staging (no fixed epochs_per_stage). Pinned: will only HOLD. +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 # unused while pinned; kept for when we un-pin later +epochs_per_stage: 25 # unused fallback + +# Log often enough to see if train AND eval both fall (matches L7/L8 cadence). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L10_train_fo_bfs.json +val_path: data/star_2arm_L10_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L12_bfs_stage0.yaml b/args/star_coconut_L12_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f5b606efae8f02d360f5316fca851c9051d14194 --- /dev/null +++ b/args/star_coconut_L12_bfs_stage0.yaml @@ -0,0 +1,52 @@ +# DIAGNOSTIC: depth-12, PINNED at stage 0. Bisecting L10 (works) -> L20 (fails). +# Single-GPU run: batch_size_training=256 to match L10's 2-GPU global batch. +project: coconut +save_path: ckpts +name: "star-coconut-L12-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +# Loss-gated staging (no fixed epochs_per_stage). Pinned: will only HOLD. +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 # unused while pinned; kept for when we un-pin later +epochs_per_stage: 25 # unused fallback + +# Log often enough to see if train AND eval both fall (matches L7/L8 cadence). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L12_train_fo_bfs.json +val_path: data/star_2arm_L12_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 256 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L12_bfs_stage0_4layer.yaml b/args/star_coconut_L12_bfs_stage0_4layer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4d136b54271603f4744a2780cee251ee08431bf3 --- /dev/null +++ b/args/star_coconut_L12_bfs_stage0_4layer.yaml @@ -0,0 +1,53 @@ +# CAPACITY TEST: L12 stage-0-pinned, but with a 4-layer model (vs the 2-layer +# used everywhere else). L12 stage 0 memorized (train->1.1, eval->5+) with 2 +# layers; if 4 layers lets eval CE drop toward ~0.7, the L12/L20 failure is a +# network-depth capacity limit on long-context associative retrieval. +project: coconut +save_path: ckpts +name: "star-coconut-L12-bfs-stage0-4layer" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +# Loss-gated staging, PINNED at stage 0 (max_latent_stage == init_stage). +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.0 +epochs_per_stage: 25 + +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-4layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L12_train_fo_bfs.json +val_path: data/star_2arm_L12_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 256 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L12_bfs_stage0_50k.yaml b/args/star_coconut_L12_bfs_stage0_50k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3dcad907928cafc407d73444db699a15767c83b1 --- /dev/null +++ b/args/star_coconut_L12_bfs_stage0_50k.yaml @@ -0,0 +1,54 @@ +# DATA-SCALING TEST: L12 stage-0-pinned, standard 2-layer model, but 50k train +# graphs instead of 14k. The 4-layer capacity test failed (same memorization), +# so the hypothesis is sample complexity: more data should make memorizing the +# train set harder than learning the general hop-1 matching rule. Success = +# eval CE drops toward ~0.7 instead of diverging to 5+. +project: coconut +save_path: ckpts +name: "star-coconut-L12-bfs-stage0-50k" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.0 +epochs_per_stage: 25 + +log_every: 2 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_50k_L12_train_fo_bfs.json +val_path: data/star_2arm_50k_L12_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 256 +debug: False +gradient_accumulation_steps: 1 +# 50k data => ~3.6x more steps/epoch than the 14k runs; 100 epochs is plenty. +num_epochs: 100 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L14_bfs_stage0.yaml b/args/star_coconut_L14_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37785ca6c22363db66b0b38ab867a0b68a73766c --- /dev/null +++ b/args/star_coconut_L14_bfs_stage0.yaml @@ -0,0 +1,52 @@ +# DIAGNOSTIC: depth-14, PINNED at stage 0. Bisecting L10 (works) -> L20 (fails). +# Single-GPU run: batch_size_training=256 to match L10's 2-GPU global batch. +project: coconut +save_path: ckpts +name: "star-coconut-L14-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +# Loss-gated staging (no fixed epochs_per_stage). Pinned: will only HOLD. +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 # unused while pinned; kept for when we un-pin later +epochs_per_stage: 25 # unused fallback + +# Log often enough to see if train AND eval both fall (matches L7/L8 cadence). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L14_train_fo_bfs.json +val_path: data/star_2arm_L14_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 256 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_backtrack.yaml b/args/star_coconut_L15_bfs_backtrack.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97d5f6baf71d3f5ddf7b83ef729c0c0b23e2f6a5 --- /dev/null +++ b/args/star_coconut_L15_bfs_backtrack.yaml @@ -0,0 +1,59 @@ +# Depth-15 2-arm star. Backtracking gated on FRONTIER accuracy (argmax lands on +# a correct depth-k node). Control for the superposition-gated arm: identical in +# every respect except `staging_metric` is left at the bfs_variant default. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-backtrack" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +# staging_metric omitted -> defaults to "frontier" (bfs_variant: True) + +eval_every: 2 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_backtrack_ce.yaml b/args/star_coconut_L15_bfs_backtrack_ce.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1e0724d8f3b6eba4ba3e020703c36ca761bf3f7b --- /dev/null +++ b/args/star_coconut_L15_bfs_backtrack_ce.yaml @@ -0,0 +1,71 @@ +# Depth-15 counterpart of star_coconut_L10_bfs_backtrack_ce.yaml: identical levers +# (CE_SCORE-gated promote + backtrack at 0.9, full BPTT, no truncation), depth 15. +# +# ONE unavoidable difference from the L10 arm: L10 starts at stage 1 from a mastered +# stage-0 checkpoint. No such checkpoint exists for L15, and the L10 one does NOT +# transfer -- evaluated on L15 prompts it scores hop-1 ce_score 0.162 (CE 6.10 vs the +# 0.693 floor, frontier mass 0.417), far under the 0.9 bar. So this run must earn +# stage 0 itself. star_coconut_L15_bfs_stage0_warm.yaml is producing a proper L15 +# stage-0 checkpoint in parallel; once it lands, rerun this with init_stage: 1 for a +# like-for-like timing comparison against L10. +# +# save_only_improve is OFF here on purpose: it gates on final-answer accuracy, which +# is ~0 during stage 0, so the previous L15 attempt trained 108 epochs and persisted +# nothing. 61 MB/epoch against 16 TB free is a worthwhile trade. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-backtrack-ce" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +staging_metric: ce_score # graded superposition; frontier/optimal/superposition still logged + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null # full BPTT, same as L10 (truncation to W=5 measured at + # only 10-18% faster and zero memory saving) + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_backtrack_ce_fast.yaml b/args/star_coconut_L15_bfs_backtrack_ce_fast.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8b3d55a3c21920d45a5a0e816b512e0aef14c840 --- /dev/null +++ b/args/star_coconut_L15_bfs_backtrack_ce_fast.yaml @@ -0,0 +1,64 @@ +# Depth-15 CE-gated backtracking, tuned to finish in ~2 hours. +# +# Same metric/gates as L10 CE (staging_metric=ce_score, thr=0.9), but: +# 1. warm-start from mastered L15 stage-0 (skip hop-1 plateau) +# 2. train_size=14000 (L10 scale; 100k was only needed to *learn* hop-1) +# 3. backprop_depth=5 (truncated BPTT; ~10-15% at deep stages) +# 4. bf16=True (~1.5-2x on these GPUs) +# Run with 2 GPUs (nproc_per_node=2) so steps/epoch ~55 instead of ~109. +# +# Budget sketch: ~15 stages x ~15-20 ep x ~20-30 s/ep ≈ 1.5-2.5 h. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-backtrack-ce-fast" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: ce_score + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: 5 + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 14000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_backtrack_frontier.yaml b/args/star_coconut_L15_bfs_backtrack_frontier.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f5027884eef8548186aaf2914afbbb024b8029e --- /dev/null +++ b/args/star_coconut_L15_bfs_backtrack_frontier.yaml @@ -0,0 +1,60 @@ +# Depth-15: backtracking gated on FRONTIER accuracy (argmax on reachable frontier), +# not CE / top-2 superposition. CE-gated L15 stalled at stage 3; frontier is the +# softer gate that worked for L10. +# +# Warm-started from mastered L15 stage-0. Full BPTT (no truncation). 30k train +# subset for speed after hop-1 is already learned. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-backtrack-frontier" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier # promote + backtrack on frontier acc + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null # full BPTT + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 30000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_backtrack_superpos.yaml b/args/star_coconut_L15_bfs_backtrack_superpos.yaml new file mode 100644 index 0000000000000000000000000000000000000000..27fabdd1955b230f6a535f4bdb14a2994ac4c553 --- /dev/null +++ b/args/star_coconut_L15_bfs_backtrack_superpos.yaml @@ -0,0 +1,61 @@ +# Depth-15 2-arm star. Backtracking gated on SUPERPOSITION accuracy +# (top-|F| logits == the full reachable frontier set, i.e. BOTH arms). +# 100k train graphs: L12 memorized at 14k and only generalized at 50k, so deeper +# depths get a larger pool. 781 steps/epoch here vs 109 at L10, but stage 0 sits on +# a long pre-generalization plateau, so the epoch cap has to stay generous. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-backtrack-superpos" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 # all three L15 arms start from scratch at stage 0 +promote_threshold: 0.9 +epochs_per_stage: 25 # unused by accuracy_staging (fallback only) + +staging_metric: superposition # gate promote+backtrack on both-arms superposition + +eval_every: 2 # 781 steps/epoch, so evaluate often +perhop_val_samples: 256 +perhop_train_samples: 64 # train-side per-hop is logging only; keep eval cheap + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_curstage.yaml b/args/star_coconut_L15_bfs_curstage.yaml new file mode 100644 index 0000000000000000000000000000000000000000..310f4149fa84bd9063f13f015c728606b4a3c7c0 --- /dev/null +++ b/args/star_coconut_L15_bfs_curstage.yaml @@ -0,0 +1,60 @@ +# Depth-15 2-arm star. No backtracking: promote as soon as the CURRENT stage's +# own hop is solved, with no earlier-stage retention requirement and no repair. +# The fair no-backtracking baseline. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-curstage" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +eval_every: 2 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: False +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True + +# Fair no-backtracking baseline: promote on the current stage's hop only. +promote_on_current_only: True diff --git a/args/star_coconut_L15_bfs_curstage_frontier.yaml b/args/star_coconut_L15_bfs_curstage_frontier.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ed9e66ea44760a6de2772f7870102c06e7727e8b --- /dev/null +++ b/args/star_coconut_L15_bfs_curstage_frontier.yaml @@ -0,0 +1,61 @@ +# Depth-15 no-backtracking baseline. Promotion uses ONLY current-stage FRONTIER +# accuracy (promote_on_current_only). Earlier hops can regress without blocking +# advancement — the fair contrast to frontier-gated backtracking. +# +# Same warm-start / data / bf16 as star_coconut_L15_bfs_backtrack_frontier.yaml. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-curstage-frontier" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 1 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier # current-hop frontier acc only (with promote_on_current_only) + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +backtrack: False +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +promote_on_current_only: True + +train_size: 30000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_curstage_frontier_finish.yaml b/args/star_coconut_L15_bfs_curstage_frontier_finish.yaml new file mode 100644 index 0000000000000000000000000000000000000000..42f0b18157ea852c51624599cc93cfbbd7615cb2 --- /dev/null +++ b/args/star_coconut_L15_bfs_curstage_frontier_finish.yaml @@ -0,0 +1,66 @@ +# Finish the L15 current-stage-only (CSO) baseline that stalled at stage 14. +# Warm-starts from the completed curstage-frontier checkpoint_2000, keeps the +# same promote gate (frontier @ 0.9, current-hop only, no backtracking), but +# adds max_epochs_per_stage so a stuck stage force-promotes after a fixed budget. +# Then polishes at stage 15 so the [A] leaf circuit is actually trained. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-curstage-frontier-finish" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 15 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 14 +promote_threshold: 0.9 +epochs_per_stage: 25 +# Soft deadline: if hop-15 frontier still < 0.9 after this many epochs at +# stage 14, force-promote to stage 15 and continue. +max_epochs_per_stage: 50 + +staging_metric: frontier +promote_on_current_only: True + +eval_every: 5 +log_every: 1 +perhop_val_samples: 256 +perhop_train_samples: 64 +save_every: 25 + +backprop_depth: null + +backtrack: False +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 30000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-curstage-frontier/checkpoint_2000 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +# 50 epochs at stage 14 (then force-promote) + ~200 polish at stage 15 +num_epochs: 250 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 50 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L15_bfs_stage0_warm.yaml b/args/star_coconut_L15_bfs_stage0_warm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..59031968065146fd5e3758264078d423d0101b4b --- /dev/null +++ b/args/star_coconut_L15_bfs_stage0_warm.yaml @@ -0,0 +1,66 @@ +# Depth-15 PINNED at stage 0 (max_latent_stage == init_stage == 0 => never promotes), +# warm-started from the depth-10 stage-0 model. +# +# Purpose: produce the mastered L15 stage-0 checkpoint that the CE-gated L15 curriculum +# needs, mirroring how star-coconut-L10-bfs-stage0/checkpoint_150 unblocked every L10 +# arm. A cold L15 stage-0 attempt ran 112 epochs and never left chance (train loss +# 4.27 -> 1.78 while val hop-1 stayed ~0.04, i.e. it memorized 100k graphs). +# +# The L10 warm start is imperfect but far better than random: on L15 prompts it already +# puts 0.417 of the mass on the hop-1 frontier versus ~0.03 for chance, so the edge-list +# lookup circuit partially transfers. What it lacks is the 20 node ids (47-66) that L15 +# uses and L10 never saw, plus calibration across 60 edges instead of 40. +# +# accuracy_staging is ON only to get per-hop ce_score logging; with max_latent_stage: 0 +# the promote branch can never fire, so this only ever prints HOLD. +project: coconut +save_path: ckpts +name: "star-coconut-L15-bfs-stage0-warm" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: ce_score # watch hop-1 ce_score climb toward 1.0 (floor CE = log 2) + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +revert_staging: False + +train_size: 0 + +save_only_improve: False # stage-0 final-answer acc is ~0, so improve-gating saves nothing +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L10-bfs-stage0/checkpoint_150 +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L15_train_fo_bfs.json +val_path: data/star_2arm_L15_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 400 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L16_bfs_stage0.yaml b/args/star_coconut_L16_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6d532fa1e97b4e975e16c978dd811d703d3d6944 --- /dev/null +++ b/args/star_coconut_L16_bfs_stage0.yaml @@ -0,0 +1,52 @@ +# DIAGNOSTIC: depth-16, PINNED at stage 0. Bisecting L10 (works) -> L20 (fails). +# Single-GPU run: batch_size_training=256 to match L10's 2-GPU global batch. +project: coconut +save_path: ckpts +name: "star-coconut-L16-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +# Loss-gated staging (no fixed epochs_per_stage). Pinned: will only HOLD. +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 # unused while pinned; kept for when we un-pin later +epochs_per_stage: 25 # unused fallback + +# Log often enough to see if train AND eval both fall (matches L7/L8 cadence). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L16_train_fo_bfs.json +val_path: data/star_2arm_L16_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 256 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs.yaml b/args/star_coconut_L20_bfs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e75afd377cd065cf960ba58f837e827f32ca4b53 --- /dev/null +++ b/args/star_coconut_L20_bfs.yaml @@ -0,0 +1,52 @@ +# depth-20 2-arm star, latent CoT (coconut) + sudoku-style backtracking rehearsal. +# needs 2 gpus (launched on 2,3) + +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 20 +pad_latent_to_max: True +eval_every: 25 + +# Truncated BPTT window for deep latent chains (user request: gradient limited by +# depth). Memory/compute of the backward pass stays bounded regardless of stage. +backprop_depth: 6 + +# ---- backtracking / rehearsal (port of hf_pull/sudoku-latent-backtracking) ---- +backtrack: True +remember_rate: 0.3 # prob of broad rehearsal (any earlier stage); else targeted [r_current..frontier] +backtrack_detect_threshold: 0.9 # per-hop frontier acc below which a stage counts as regressed -> r_current +revert_staging: False # use rehearsal (soft) rather than the hard revert + +# full dataset now that per-step latency is ~flat across depth (train_size: 0 = all) +train_size: 0 +perhop_train_samples: 128 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_train_fo_bfs.json +val_path: data/star_2arm_L20_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +# 25 epochs/stage * 21 stages (0..20) + 50-epoch backtracking budget at the frontier +num_epochs: 575 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_accstage.yaml b/args/star_coconut_L20_bfs_accstage.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0b7c2f62378ebedbc8f0d6b1b9583a482690336e --- /dev/null +++ b/args/star_coconut_L20_bfs_accstage.yaml @@ -0,0 +1,84 @@ +# depth-20 2-arm star, latent CoT (coconut) with ACCURACY-GATED curriculum +# promotion + sudoku-style backtracking rehearsal. needs 2 gpus. +# +# Difference vs star_coconut_L20_bfs.yaml: the latent frontier no longer advances +# on a fixed epochs-per-stage schedule. Instead `accuracy_staging` promotes to the +# next stage only once every stage 1..cur_stage has reached `promote_threshold` +# frontier accuracy. Backtracking holds/repairs any earlier stage that regresses. + +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-accstage" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +# MUST start at 0 (the no-latent CoT warmup). The L6 run that worked spends its first +# stage at scheduled_stage 0, learning the graph traversal with explicit reasoning +# tokens (this is what drove hop-1 frontier acc to 1.0 and loss to <1). Starting at 1 +# skips that foundation, so the model jumps straight to "reason through a latent" from +# random init and never bootstraps -> it froze at the unigram prior (loss ~ln(100)). +init_stage: 0 # start with the CoT warmup, then promote 0->1->...->20 +promote_threshold: 0.9 # advance only when min frontier acc over 1..cur_stage >= this +epochs_per_stage: 25 # unused by accuracy_staging, kept for reference/fallback + +# Evaluate often so promotion is responsive (the per-hop eval is the promotion signal). +eval_every: 5 +perhop_val_samples: 256 # cap val per-hop eval cost (None = full val set) +perhop_train_samples: 128 + +# NOTE: backprop_depth MUST cover the reasoning chain, otherwise learning breaks. +# With pad_latent_to_max=True the sequence always has max_latent_stage (=20) latent +# tokens even at stage 1, so max_n_latents=20 for every stage. A small window like 1 +# detaches 19/20 latent passes -> gradient never reaches the graph -> the model froze +# at the unigram prior (loss ~ln(100), constant output, chance acc). L6 used FULL BPTT +# and learned. For this tiny (2-layer/768d) model full BPTT over 20 passes is cheap, +# so use it. (If we later go much deeper and hit memory, reintroduce a window that is +# >= the current stage, not a fixed 1.) +backprop_depth: null + +# ---- backtracking / rehearsal (port of hf_pull/sudoku-latent-backtracking) ---- +backtrack: True +remember_rate: 0.3 # prob of broad rehearsal (any earlier stage); else targeted [r_current..frontier] +backtrack_detect_threshold: 0.9 # per-hop frontier acc below which a stage counts as regressed -> r_current +revert_staging: False # use rehearsal (soft) rather than the hard revert + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_train_fo_bfs.json +val_path: data/star_2arm_L20_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +# Budget cap (not a schedule): accuracy-gating decides actual stage timing. +num_epochs: 2000 +# --- training stabilization --- +# History: lr 3e-5 diverged ONLY because grad clipping was a silent no-op under FSDP +# (sharded params -> local-shard norm); fixed in run.py (FSDP.clip_grad_norm_). +# Then lr 1e-5 + working clip was STABLE but froze at the unigram prior (loss ~ln(100), +# constant output) -> under-training. With clipping now a real safety net (global norm +# capped at grad_clip), we restore the L6-proven lr so updates are strong enough to +# actually learn hop-1 retrieval, while the clip prevents the earlier divergence. +lr: !!float "1e-4" # back to the rate L6 learned with; clip keeps it stable +grad_clip: !!float "1.0" # global grad-norm clip (now FSDP-correct) = safety net +warmup_steps: 200 # linear LR warmup over first 200 optimizer steps +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_backtrack.yaml b/args/star_coconut_L20_bfs_backtrack.yaml new file mode 100644 index 0000000000000000000000000000000000000000..96ea7b05edb019a1af0592f9b17d84fcdc430e61 --- /dev/null +++ b/args/star_coconut_L20_bfs_backtrack.yaml @@ -0,0 +1,58 @@ +# Depth-20 2-arm star. Backtracking gated on FRONTIER accuracy (the previous +# metric). Control for the superposition-gated L20 arm. +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-backtrack" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +# staging_metric omitted -> defaults to "frontier" (bfs_variant: True) + +eval_every: 2 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_backtrack_frontier.yaml b/args/star_coconut_L20_bfs_backtrack_frontier.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2885cdfc8c0d4b9e0489f1672f4ca30d99454210 --- /dev/null +++ b/args/star_coconut_L20_bfs_backtrack_frontier.yaml @@ -0,0 +1,57 @@ +# Depth-20 frontier-gated backtracking — same levers as L15 frontier BT. +# No L20 stage-0 ckpt yet, so warm-start from L15 stage-0 and begin at init_stage 0 +# (hop-1 must adapt to the larger L20 node pool / 80-edge prompts). +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-backtrack-frontier" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 30000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_backtrack_superpos.yaml b/args/star_coconut_L20_bfs_backtrack_superpos.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7f379236bea05891472bd649ab6efa0fce28eddf --- /dev/null +++ b/args/star_coconut_L20_bfs_backtrack_superpos.yaml @@ -0,0 +1,59 @@ +# Depth-20 2-arm star (max depth the 100-node vocab supports: 2+4*20 = 82 nodes). +# Backtracking gated on SUPERPOSITION accuracy (top-|F| == full frontier set). +# 100k train graphs; prompt is 266 tokens, well inside n_positions=1024. +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-backtrack-superpos" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: superposition + +eval_every: 2 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: True +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_curstage.yaml b/args/star_coconut_L20_bfs_curstage.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e4fac8ba8b6cd0e91281a1d4cb5984cbc8befcec --- /dev/null +++ b/args/star_coconut_L20_bfs_curstage.yaml @@ -0,0 +1,59 @@ +# Depth-20 2-arm star. No backtracking: promote on the current stage's hop only. +# The fair no-backtracking baseline at L20. +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-curstage" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +# ---- accuracy-gated curriculum promotion ---- +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +eval_every: 2 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +# ---- backtracking / rehearsal ---- +backtrack: False +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +train_size: 0 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True + +# Fair no-backtracking baseline: promote on the current stage's hop only. +promote_on_current_only: True diff --git a/args/star_coconut_L20_bfs_curstage_frontier.yaml b/args/star_coconut_L20_bfs_curstage_frontier.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2c3ae6fa218807a6391201fcc0a8a23a6070001a --- /dev/null +++ b/args/star_coconut_L20_bfs_curstage_frontier.yaml @@ -0,0 +1,58 @@ +# Depth-20 no-backtracking baseline — promote on current-stage frontier only. +# Same warm-start / data / bf16 as star_coconut_L20_bfs_backtrack_frontier.yaml. +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-curstage-frontier" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 20 +pad_latent_to_max: True + +accuracy_staging: True +init_stage: 0 +promote_threshold: 0.9 +epochs_per_stage: 25 + +staging_metric: frontier + +eval_every: 5 +perhop_val_samples: 256 +perhop_train_samples: 64 + +backprop_depth: null + +backtrack: False +remember_rate: 0.3 +backtrack_detect_threshold: 0.9 +revert_staging: False + +promote_on_current_only: True + +train_size: 30000 + +save_only_improve: False +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: ckpts/star-coconut-L15-bfs-stage0-warm/checkpoint_150 +seed: 0 +resume: 0 +bf16: True +train_path: data/star_2arm_L20_100k_train_fo_bfs.json +val_path: data/star_2arm_L20_100k_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2000 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_plain.yaml b/args/star_coconut_L20_bfs_plain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc64f6d1b32c62be923407f029109f6fb7bcd74f --- /dev/null +++ b/args/star_coconut_L20_bfs_plain.yaml @@ -0,0 +1,47 @@ +# DIAGNOSTIC: the EXACT recipe that just learned at depth 6 (star_coconut_L6_bfs_v128), +# changed ONLY to depth 20 + the L20 data. No accuracy-staging, NO backtracking, full +# BPTT, epoch-based staging, lr 1e-4. This isolates the user's question ("why is 20 +# different from 6?") by holding the training recipe fixed and varying only depth/data. +# +# If THIS learns -> the L20 failure was caused by the accuracy_staging/backtrack code +# path (not depth/data), and we fix that path. +# If THIS also stalls -> depth-20 / the regenerated L20 data is intrinsically the issue +# (e.g. 20 latents too hard for stage 0, or a data-generation problem), and we inspect +# the data + stage-0 target. +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-plain" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 20 +pad_latent_to_max: True +eval_every: 25 + +# no backtrack, no accuracy_staging, no backprop_depth (full BPTT) -> identical knobs to +# the depth-6 run that worked. + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_train_fo_bfs.json +val_path: data/star_2arm_L20_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 575 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L20_bfs_stage0.yaml b/args/star_coconut_L20_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3dba20f63a996c7251ad5f54a2d753b68cc1e116 --- /dev/null +++ b/args/star_coconut_L20_bfs_stage0.yaml @@ -0,0 +1,53 @@ +# DIAGNOSTIC: depth-20, PINNED at stage 0. Loss-gated curriculum is ON, but +# max_latent_stage == init_stage == 0 so we never promote — just watch whether +# stage-0 eval CE decreases (same hop-1 neighbor target as L6 stage 0). +project: coconut +save_path: ckpts +name: "star-coconut-L20-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +# Loss-gated staging (no fixed epochs_per_stage). Pinned: will only HOLD. +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 # unused while pinned; kept for when we un-pin later +epochs_per_stage: 25 # unused fallback + +# Log often enough to see if train AND eval both fall (matches L7/L8 cadence). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L20_train_fo_bfs.json +val_path: data/star_2arm_L20_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L6_bfs.yaml b/args/star_coconut_L6_bfs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c1e264d00399aef15257d6d198bcd4f0493dce75 --- /dev/null +++ b/args/star_coconut_L6_bfs.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-coconut-L6-bfs" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True +eval_every: 25 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_bfs.json +val_path: data/star_2arm_L6_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 200 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L6_bfs_stage0.yaml b/args/star_coconut_L6_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e3564030eb028fb67d81484d918e4a436c82da22 --- /dev/null +++ b/args/star_coconut_L6_bfs_stage0.yaml @@ -0,0 +1,50 @@ +# DIAGNOSTIC: depth-6, PINNED at stage 0 — same knobs as L10/L20 stage0 runs. +# Fair comparison: if L6 loss collapses here but L10/L20 did not, the gap is +# depth/data (edge-list length), not curriculum promotion. +project: coconut +save_path: ckpts +name: "star-coconut-L6-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 +epochs_per_stage: 25 + +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_bfs.json +val_path: data/star_2arm_L6_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 200 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L6_bfs_v128.yaml b/args/star_coconut_L6_bfs_v128.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0b8b98b33e27450201c1b99ce17abef4003e388b --- /dev/null +++ b/args/star_coconut_L6_bfs_v128.yaml @@ -0,0 +1,41 @@ +# DIAGNOSTIC: pristine depth-6 L6 recipe (the config that learned before) run through +# the CURRENT code path (updated run.py / coconut.py / 100-node tokenizer). Only change +# vs the original star_coconut_L6_bfs.yaml is model_id -> the vocab-128 model, so the +# special-token ids (now 100..108 under NUM_NODES=100) are representable. Epoch-based +# staging + full BPTT, exactly like the run that worked. If THIS learns, the shared code +# is fine and the L20 failure is depth/data/accuracy-staging specific; if it ALSO stalls, +# we broke something shared. +project: coconut +save_path: ckpts +name: "star-coconut-L6-bfs-v128" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True +eval_every: 25 + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_bfs.json +val_path: data/star_2arm_L6_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 200 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L7_bfs_stage0.yaml b/args/star_coconut_L7_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0b7f115fa284d322d08ee2bb7333e96e15323c5e --- /dev/null +++ b/args/star_coconut_L7_bfs_stage0.yaml @@ -0,0 +1,51 @@ +# DIAGNOSTIC: depth-7, PINNED at stage 0 — same recipe as L6 stage0. +# Bisect between L6 (works) and L10 (fails). +project: coconut +save_path: ckpts +name: "star-coconut-L7-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 +epochs_per_stage: 25 + +# Log often enough to see if train AND eval both fall (the L10 failure mode). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L7_train_fo_bfs.json +val_path: data/star_2arm_L7_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_L8_bfs_stage0.yaml b/args/star_coconut_L8_bfs_stage0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8763f06d93af87c429dba4916ff56ceb3827ece7 --- /dev/null +++ b/args/star_coconut_L8_bfs_stage0.yaml @@ -0,0 +1,51 @@ +# DIAGNOSTIC: depth-8, PINNED at stage 0 — same recipe as L6 stage0. +# Bisect between L6 (works) and L10 (fails). +project: coconut +save_path: ckpts +name: "star-coconut-L8-bfs-stage0" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +max_latent_stage: 0 +pad_latent_to_max: True + +loss_staging: True +init_stage: 0 +promote_loss_threshold: 1.5 +epochs_per_stage: 25 + +# Log often enough to see if train AND eval both fall (the L10 failure mode). +log_every: 5 +eval_every: 50 +perhop_val_samples: 256 +perhop_train_samples: 128 + +backprop_depth: null +backtrack: False +accuracy_staging: False + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim-L20.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L8_train_fo_bfs.json +val_path: data/star_2arm_L8_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +grad_clip: !!float "1.0" +warmup_steps: 200 +weight_decay: 0.01 +bfs_variant: True diff --git a/args/star_coconut_full.yaml b/args/star_coconut_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..41396c50f614a726884245fd11b0a2dda8c8c358 --- /dev/null +++ b/args/star_coconut_full.yaml @@ -0,0 +1,35 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-2arm-L6-full" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_coconut.json +val_path: data/star_2arm_L6_valid_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False diff --git a/args/star_coconut_revert.yaml b/args/star_coconut_revert.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ddf73c6af6b91c70f140f83b64ef5d6fe46ff9ad --- /dev/null +++ b/args/star_coconut_revert.yaml @@ -0,0 +1,35 @@ +project: coconut +save_path: ckpts +name: "star-2arm-L6-revert" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_coconut.json +val_path: data/star_2arm_L6_valid_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +revert_staging: True +revert_threshold: 0.8 +revert_metric: frontier diff --git a/args/star_finalonly_bfs.yaml b/args/star_finalonly_bfs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e229b01fb2b32379faa8db914c8dccd60b2a966b --- /dev/null +++ b/args/star_finalonly_bfs.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-2arm-L6-fo-bfs" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_bfs.json +val_path: data/star_2arm_L6_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_finalonly_full.yaml b/args/star_finalonly_full.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d154f3036b304a19f9be2099be4e3c700616579f --- /dev/null +++ b/args/star_finalonly_full.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-2arm-L6-fo" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.1 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_coconut.json +val_path: data/star_2arm_L6_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_finalonly_revert.yaml b/args/star_finalonly_revert.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d0dbdfe70a59935b9e727176bddb88c912338283 --- /dev/null +++ b/args/star_finalonly_revert.yaml @@ -0,0 +1,39 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-2arm-L6-fo-rev" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_coconut.json +val_path: data/star_2arm_L6_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True +revert_staging: True +revert_metric: acc +revert_threshold: 0.8 diff --git a/args/star_finalonly_revert_bfs.yaml b/args/star_finalonly_revert_bfs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2f67ca13ed3475a5e4fa92d4e556d16cd7dc268 --- /dev/null +++ b/args/star_finalonly_revert_bfs.yaml @@ -0,0 +1,39 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-2arm-L6-fo-rev-bfs" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_2arm_L6_train_fo_bfs.json +val_path: data/star_2arm_L6_valid_fo_bfs.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 300 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True +revert_staging: True +revert_metric: acc +revert_threshold: 0.8 diff --git a/args/star_fo_L2_100k_rev.yaml b/args/star_fo_L2_100k_rev.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3a130004e6c20bf1a46b5664ce2c97527fa742fb --- /dev/null +++ b/args/star_fo_L2_100k_rev.yaml @@ -0,0 +1,39 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-L2-100k-rev" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 1 +max_latent_stage: 2 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_L2_train100k_fo_coconut.json +val_path: data/star_L2_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 100 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True +revert_staging: True +revert_metric: acc +revert_threshold: 0.8 diff --git a/args/star_fo_L3_100k_rev.yaml b/args/star_fo_L3_100k_rev.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1dc3d17648237be3210061c1a5bb1945f42c4ea1 --- /dev/null +++ b/args/star_fo_L3_100k_rev.yaml @@ -0,0 +1,39 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-L3-100k-rev" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 1 +max_latent_stage: 3 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_L3_100k_train_fo_coconut.json +val_path: data/star_L3_100k_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 120 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True +revert_staging: True +revert_metric: acc +revert_threshold: 0.8 diff --git a/args/star_fo_L6_100k.yaml b/args/star_fo_L6_100k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1cdb05a6a24b3c6cf27c2004ee172af3c0e1dea6 --- /dev/null +++ b/args/star_fo_L6_100k.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-L6-100k" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 1 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 1.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_L6_100k_train_fo_coconut.json +val_path: data/star_L6_100k_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 40 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_fo_L6_100k_rev.yaml b/args/star_fo_L6_100k_rev.yaml new file mode 100644 index 0000000000000000000000000000000000000000..127ba1fad5c94899ea8c0d22f806f32e8a13dab1 --- /dev/null +++ b/args/star_fo_L6_100k_rev.yaml @@ -0,0 +1,39 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-L6-100k-rev" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 1 +max_latent_stage: 6 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_L6_100k_train_fo_coconut.json +val_path: data/star_L6_100k_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 150 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True +revert_staging: True +revert_metric: acc +revert_threshold: 0.8 diff --git a/args/star_fo_d1_14000.yaml b/args/star_fo_d1_14000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4f789e47ca78973d98bc53a0517c9ec01a3ea35 --- /dev/null +++ b/args/star_fo_d1_14000.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-14000" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train14000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_fo_d1_240000.yaml b/args/star_fo_d1_240000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..edbede40b3c8a0d01e2582fb86226e2bbb8302e0 --- /dev/null +++ b/args/star_fo_d1_240000.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-240000" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train240000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 8 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_fo_d1_240k_long.yaml b/args/star_fo_d1_240k_long.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3f3efa1989bfe4ecd9f76c81d8ffd169b776e8a3 --- /dev/null +++ b/args/star_fo_d1_240k_long.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-240k-long" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train240000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 30 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_fo_d1_3000000.yaml b/args/star_fo_d1_3000000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b60edabf2e303002c6cb337ff9beb62187f95cd7 --- /dev/null +++ b/args/star_fo_d1_3000000.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-3000000" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train3000000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 2 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_fo_d1_30k_rev.yaml b/args/star_fo_d1_30k_rev.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa1518d6d210458b3dd3ee989c3fde902bbcea7e --- /dev/null +++ b/args/star_fo_d1_30k_rev.yaml @@ -0,0 +1,39 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-30k-rev" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 1 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train30000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 80 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True +revert_staging: True +revert_metric: acc +revert_threshold: 0.8 diff --git a/args/star_fo_d1_60000.yaml b/args/star_fo_d1_60000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8b1fba6f82bb1bd5de46af4ae1ae84f86b4636e5 --- /dev/null +++ b/args/star_fo_d1_60000.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-60000" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train60000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 33 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/args/star_fo_d1_960000.yaml b/args/star_fo_d1_960000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c63a51785dac325724eff605ebffdd9cb3d5a9c9 --- /dev/null +++ b/args/star_fo_d1_960000.yaml @@ -0,0 +1,36 @@ +# need 2 gpus + +project: coconut +save_path: ckpts +name: "star-fo-d1-960000" + +only_eval: False + +coconut: True +cot: False +no_thoughts: False +no_cot: False + +c_thought: 1 +epochs_per_stage: 25 +max_latent_stage: 1 +pad_latent_to_max: True + +save_only_improve: True +uniform_prob: 0.0 +model_id: configs/symbol-2layer-8head-768dim.json +load_model_path: None +seed: 0 +resume: 0 +bf16: False +train_path: data/star_d1_train960000_fo_coconut.json +val_path: data/star_d1_valid_fo_coconut.json +reset_optimizer: False +batch_size_training: 128 +debug: False +gradient_accumulation_steps: 1 +num_epochs: 3 +lr: !!float "1e-4" +weight_decay: 0.01 +bfs_variant: False +final_only: True diff --git a/coconut.py b/coconut.py new file mode 100644 index 0000000000000000000000000000000000000000..854f4c99e1800922a06d88ecceb310fa2f98c08a --- /dev/null +++ b/coconut.py @@ -0,0 +1,277 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss +from collections import namedtuple +from transformers.models.gpt2 import GPT2LMHeadModel + +Outputs = namedtuple("Outputs", ["loss", "inputs_embeds", "logits"]) +MAX_N_LATENT = 8 + + +class Coconut(nn.Module): + + def __init__( + self, + base_causallm, + latent_token_id, + start_latent_id, + end_latent_id, + eos_token_id, + backprop_depth=None, + ): + + super(Coconut, self).__init__() + self.gen_forward_cnt = 0 + self.base_causallm = base_causallm + self.latent_token_id = latent_token_id + self.eos_token_id = eos_token_id + self.start_latent_id = start_latent_id + self.end_latent_id = end_latent_id + # Truncated BPTT window: if set to W, gradients only flow through the last W + # latent recurrence steps (+ the final answer pass); older latent states and + # their KV cache are detached. This keeps backward compute + activation memory + # O(W) instead of O(depth), enabling higher graph depth at ~constant latency. + # None => full BPTT (upstream behavior). + self.backprop_depth = backprop_depth + + # tested with GPT2 and Llama3 + if isinstance(self.base_causallm, GPT2LMHeadModel): + self.embedding = self.base_causallm.transformer.get_input_embeddings() + else: + self.embedding = self.base_causallm.get_input_embeddings() + + def forward(self, input_ids, attention_mask, labels, position_ids, **kwargs): + + logits = [] + + latent_indices = ( + input_ids == self.latent_token_id + ).nonzero() # (num_latent_tokens_in_the_batch, 2) + + # Group latent positions per batch instance. The upstream version did this with + # a nested Python loop that called `.item()` and compared GPU scalars for every + # (instance x latent) pair -> O(bs^2 * depth) GPU->CPU syncs per forward, which + # (not the model compute or backprop) was the dominant cost and made deep latent + # chains explode. We transfer the index pairs to host ONCE and group in pure Python. + latent_lists = [[] for _ in range(input_ids.shape[0])] + for r, c in latent_indices.tolist(): + latent_lists[r].append(c) # nonzero() already returns row-major order + + max_n_latents = max([len(l) for l in latent_lists]) + + next_compute_range = (0, input_ids.shape[1]) + inputs_embeds = self.embedding(input_ids) + + if max_n_latents > 0: + next_compute_range = (0, latent_indices[:, 1].min().item()) + # before the earliest latent token position + + kv_cache = None + + for pass_idx in range(max_n_latents): + + if kv_cache == None: + # first forward pass + outputs = self.base_causallm( + inputs_embeds=inputs_embeds[ + :, next_compute_range[0] : next_compute_range[1], : + ], + attention_mask=attention_mask[ + :, next_compute_range[0] : next_compute_range[1] + ], + position_ids=position_ids[ + :, next_compute_range[0] : next_compute_range[1] + ], + output_hidden_states=True, + ) + hidden_states_offset = 0 + + else: + # extract kv cache to reuse + past_key_values = [ + ( + k[:, :, : next_compute_range[0], :], + v[:, :, : next_compute_range[0], :], + ) + for k, v in kv_cache + ] + + outputs = self.base_causallm( + inputs_embeds=inputs_embeds[ + :, next_compute_range[0] : next_compute_range[1], : + ], + attention_mask=attention_mask[:, : next_compute_range[1]], + position_ids=position_ids[ + :, next_compute_range[0] : next_compute_range[1] + ], + past_key_values=past_key_values, + output_hidden_states=True, + ) + + hidden_states_offset = next_compute_range[0] + # when we use kv_cache for the first k tokens + # in `outputs.hidden_states`, [0, k) will be skipped + # so we need to keep this offset to correctly use the last hidden states + + logits.append(outputs.logits) + + next_compute_range = ( + next_compute_range[1], + ( + input_ids.shape[1] + if pass_idx + 1 >= max_n_latents + else next_compute_range[1] + 1 + ), + ) + + hidden_states = outputs.hidden_states[ + -1 + ] # Get the last layer hidden states + kv_cache = outputs.past_key_values + + # Truncated BPTT: for passes older than `backprop_depth` steps from the end, + # detach the fed-back thought and the KV cache so the autograd graph only + # retains the last W recurrent steps. `passes_remaining` counts steps after + # this one; when it is >= W this pass falls outside the window. + if self.backprop_depth is not None: + passes_remaining = max_n_latents - 1 - pass_idx + if passes_remaining >= self.backprop_depth: + hidden_states = hidden_states.detach() + if kv_cache is not None: + kv_cache = [ + (k.detach(), v.detach()) for (k, v) in kv_cache + ] + + # feedback the continuous thoughts to the input_embeds + + # first decide the positions to feedback + filling_indices = [ + (instance_idx, mask_list[pass_idx]) + for instance_idx, mask_list in enumerate(latent_lists) + if len(mask_list) > pass_idx + ] + + # Vectorized continuous-thought feedback (equivalent to the upstream + # per-element rebuild, but ~batch*seq times fewer GPU ops). The upstream + # code materialized batch*seq individual 1-D tensor views in Python every + # latent pass, which dominates runtime on this small synthetic model and + # scales with the number of latents. We instead clone once (to stay + # autograd-safe / out-of-place w.r.t. the tensor still needed for backward) + # and scatter the preceding hidden states into the latent positions. + if filling_indices: + b_idx = torch.tensor( + [b for b, _ in filling_indices], device=inputs_embeds.device + ) + t_idx = torch.tensor( + [t for _, t in filling_indices], device=inputs_embeds.device + ) + src = hidden_states[b_idx, t_idx - 1 - hidden_states_offset, :] + inputs_embeds = inputs_embeds.clone() + inputs_embeds[b_idx, t_idx, :] = src + + # final pass + outputs = self.base_causallm( + inputs_embeds=inputs_embeds[ + :, next_compute_range[0] : next_compute_range[1], : + ], + attention_mask=attention_mask[:, : next_compute_range[1]], + position_ids=position_ids[:, next_compute_range[0] : next_compute_range[1]], + past_key_values=( + [ + ( + k[:, :, : next_compute_range[0], :], + v[:, :, : next_compute_range[0], :], + ) + for k, v in kv_cache + ] + if kv_cache + else None + ), + output_hidden_states=True, + ) + + logits.append(outputs.logits) + + self.gen_forward_cnt += max_n_latents + 1 + + logits = torch.cat(logits, dim=-2) + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1) + ) + + return Outputs(loss=loss, inputs_embeds=inputs_embeds, logits=logits) + + def train(self): + self.base_causallm.train() + + def eval(self): + self.base_causallm.eval() + + def generate( + self, + input_ids, + attention_mask, # attention_mask is not used + max_new_tokens=16, + output_embedding=False, + synced_gpus=False, + **kwargs + ): + + self.gen_forward_cnt = 0 + + assert input_ids.shape[0] == 1, "only support batch_size == 1 now" + + tokens = input_ids[0].detach().tolist() + + labels = input_ids.clone() # placeholder. not used. + outputs = self.forward( + input_ids, + torch.ones_like(input_ids, device=input_ids.device), + labels, + torch.arange( + 0, input_ids.shape[1], dtype=torch.long, device=input_ids.device + ).reshape(1, -1), + ) + inputs_embeds = outputs.inputs_embeds + + # get the first token using the current hidden state + next_token = torch.argmax(outputs.logits[0, -1]).item() + tokens.append(next_token) + new_token_embed = self.embedding( + torch.tensor(next_token, device=input_ids.device) + ).view(1, 1, -1) + new_inputs_embeds = torch.cat((inputs_embeds, new_token_embed), dim=1) + + # get other tokens + for _ in range(max_new_tokens - 1): + outputs = self.base_causallm(inputs_embeds=new_inputs_embeds) + self.gen_forward_cnt += 1 + next_token = torch.argmax(outputs.logits[0, -1]).item() + if next_token == self.eos_token_id: + break + tokens.append(next_token) + new_token_embed = self.embedding( + torch.tensor(next_token, device=input_ids.device) + ).view(1, 1, -1) + new_inputs_embeds = torch.cat((new_inputs_embeds, new_token_embed), dim=1) + + if synced_gpus: + # in FSDP, the number of forward pass need to be the same across devices + while ( + self.gen_forward_cnt < max_new_tokens + MAX_N_LATENT + ): # leave some room for latent tokens + self.gen_forward_cnt += 1 + _ = self.base_causallm(inputs_embeds=new_inputs_embeds) + + if output_embedding: + # for analysis purpose + return torch.tensor(tokens).view(1, -1), new_inputs_embeds + + else: + return torch.tensor(tokens).view(1, -1) diff --git a/configs/symbol-2layer-8head-128dim.json b/configs/symbol-2layer-8head-128dim.json new file mode 100644 index 0000000000000000000000000000000000000000..af78f4419800b06c34384f00d9bc73d7177829ec --- /dev/null +++ b/configs/symbol-2layer-8head-128dim.json @@ -0,0 +1,38 @@ +{ + "_name_or_path": "gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 35, + "embd_pdrop": 0.1, + "eos_token_id": 0, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 128, + "n_head": 8, + "n_inner": null, + "n_layer": 2, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "transformers_version": "4.47.0", + "use_cache": true, + "vocab_size": 40 +} \ No newline at end of file diff --git a/configs/symbol-2layer-8head-768dim-L20.json b/configs/symbol-2layer-8head-768dim-L20.json new file mode 100644 index 0000000000000000000000000000000000000000..ce28e10ed9ae22a75673b5aec11631b6986991ec --- /dev/null +++ b/configs/symbol-2layer-8head-768dim-L20.json @@ -0,0 +1,38 @@ +{ + "_name_or_path": "gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 104, + "embd_pdrop": 0.1, + "eos_token_id": 107, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 768, + "n_head": 8, + "n_inner": null, + "n_layer": 2, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "transformers_version": "4.47.0", + "use_cache": true, + "vocab_size": 128 +} diff --git a/configs/symbol-2layer-8head-768dim.json b/configs/symbol-2layer-8head-768dim.json new file mode 100644 index 0000000000000000000000000000000000000000..f82fad68cc86781184a479a3abb4949801bcc21c --- /dev/null +++ b/configs/symbol-2layer-8head-768dim.json @@ -0,0 +1,38 @@ +{ + "_name_or_path": "gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 35, + "embd_pdrop": 0.1, + "eos_token_id": 0, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 768, + "n_head": 8, + "n_inner": null, + "n_layer": 2, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "transformers_version": "4.47.0", + "use_cache": true, + "vocab_size": 40 +} diff --git a/configs/symbol-4layer-8head-768dim-L20.json b/configs/symbol-4layer-8head-768dim-L20.json new file mode 100644 index 0000000000000000000000000000000000000000..3fff18cd0f9719db9af312241d0d3fcf6bb2a6fe --- /dev/null +++ b/configs/symbol-4layer-8head-768dim-L20.json @@ -0,0 +1,38 @@ +{ + "_name_or_path": "gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 104, + "embd_pdrop": 0.1, + "eos_token_id": 107, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 768, + "n_head": 8, + "n_inner": null, + "n_layer": 4, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "transformers_version": "4.47.0", + "use_cache": true, + "vocab_size": 128 +} diff --git a/cursor_depth_2p_runs_status.md b/cursor_depth_2p_runs_status.md new file mode 100644 index 0000000000000000000000000000000000000000..5819b563a4b0944efc08c83fdc2669c346175ba8 --- /dev/null +++ b/cursor_depth_2p_runs_status.md @@ -0,0 +1,2506 @@ +# Depth 2p runs status +_Exported on 7/22/2026 at 20:46:32 PDT from Cursor (3.1.17)_ + +--- + +**User** + +Yeah, I think your training it takes for very less amount of time and we need to make the promotion of it stage accuracy dependent instead of iteration dependent. So maybe the runs that are going and are not improving, please kill those runs and then make it accuracy dependent and check how much time does it so and check if it reaches stage one accuracy, stage two accuracy, and then so on. So do this, and also then you also have the backtracking implemented for this, right? Where you actually check all the previous stages and then improve those stages if any stage falls below. I think this is where backtracking, backtracking might help + +--- + +**Cursor** + +I'll tackle this in order: kill the non-improving runs, then rework the curriculum so stage promotion is accuracy-gated (not epoch-gated), while keeping your backtracking. Let me set up a plan and start by getting the shell working and reading the promotion logic. + +While I work on the shell, let me read the promotion logic in parallel. First let me try to kill the runs with a guarded command and read the key code. + +The shell is wedged (pre-existing hang carried over from the prior session — my commands are queuing behind a stuck worker). Let me check the terminal state files directly to confirm. + +The shell is fully wedged again (even `ls` hangs) — this is the same unrecoverable state carried over from the prior session, so I can't kill/relaunch until it's freed. But I can do all the substantive code work now (making promotion accuracy-gated), so the moment the shell is back we just relaunch. Let me read the relevant `run.py` sections. + +Now let me read the existing L20 config and the `get_graph_latent_cot_dataset_backtrack` builder so my changes integrate cleanly. + +Let me check the `perhop_categorize` signature (to cap eval cost) and confirm `time` is imported in `run.py`. + +I have everything I need. Let me implement the accuracy-gated curriculum. First, add `time` import. + +Now add the accuracy-staging state before the training loop. + +Now the stage-selection branch: + +Now cap the val per-hop eval cost and add the promotion logic. Let me update the per-hop eval block. + +Now add the promotion logic right after the backtracking block: + +Now let me create the accuracy-gated depth-20 config (distinct name so it won't clobber the old run's checkpoints/logs). + +Let me check for lint errors in the edited `run.py`. + +Code changes are complete and lint-clean. Let me update progress and try the shell again to kill the runs. + +The shell is still wedged (unrecoverable from inside the agent — same recurring bad-GPU CUDA-init hang from the prior session; every command, even `echo`, queues behind the stuck worker). Let me confirm exactly how `run.py` takes its config so I can give you the precise relaunch command. + +The code work is done and lint-clean. The only blocker is the wedged shell (same recurring issue from your session history), so I couldn't kill/relaunch myself. Here's the full status. + +## What I changed (accuracy-gated curriculum + backtracking) + +Previously the frontier advanced purely on the epoch counter (`scheduled_stage = epoch // epochs_per_stage`) — so the depth-20 run marched to frontier=13 while frontier accuracy sat at ~0.02–0.04 at every hop. I made promotion **accuracy-driven** instead. + +In `reasoning-by-superposition-main/run.py`: +- New `accuracy_staging` mode: `scheduled_stage = cur_stage`, a persistent frontier that only advances when earned. +- Promotion rule (after each per-hop eval): advance `cur_stage → cur_stage+1` **only when `min(frontier_acc[1..cur_stage]) >= promote_threshold`** (default 0.9). This checks the current stage *and all previous stages* — so a regression anywhere below the frontier blocks promotion. +- Backtracking is preserved and now does real work: when an earlier stage drops below `backtrack_detect_threshold`, `bt_r_current` points at the earliest regressed stage and the rehearsal sampler retrains `[r_current..frontier]` until it recovers — exactly the "go back and fix the weak stage" behavior you described. +- Per-stage timing: it logs epochs + wall-clock to solve each stage (`[acc-stage] PROMOTE stage k -> k+1 | solved in N epochs / Ts`), so you get your "how long to reach stage 1, stage 2, …" measurement directly. +- Capped val per-hop eval (`perhop_val_samples`) so frequent eval stays cheap. +- `backprop_depth: 6` kept (limited gradient by depth, per your earlier requirement). + +New config: `args/star_coconut_L20_bfs_accstage.yaml` (`accuracy_staging: True`, `promote_threshold: 0.9`, `eval_every: 5`, `init_stage: 1`, distinct `name`/log so it won't clobber the old run). + +## The blocker: shell is wedged + +Every command (even `echo`) hangs — the shell worker is stuck on the prior session's unkillable CUDA-init (bad GPU). I can't kill the old L20 run or launch the new one until it's freed. Two options: + +- **You unwedge it** (kill the stuck child from a separate terminal, or Reload Window) and then just send a message — I'll immediately kill the old runs and launch the accuracy-gated one, and watch the stage-1/2 timing. +- **Or run it yourself:** + +```bash +# 1. kill the old (non-improving) runs +pkill -9 -f 'star_coconut_L20_bfs' ; pkill -9 -f 'run.py' + +# 2. launch the accuracy-gated depth-20 run (superposition env, GPUs 2,3) +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main +CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline \ +nohup conda run -n superposition torchrun --nproc_per_node 2 \ + run.py args/star_coconut_L20_bfs_accstage.yaml \ + > logs/star_coconut_L20_bfs_accstage.log 2>&1 & +``` + +Then watch promotions with: `grep acc-stage logs/star_coconut_L20_bfs_accstage.log`. + +One thing worth flagging: the old run showed even **hop-1 stuck at ~0.03**, which is suspicious (L6 solved hop 1 instantly). With accuracy-gating it now simply won't leave stage 1 until hop 1 is actually solved — so if hop 1 genuinely can't get above 0.9 quickly, that will surface immediately (in the first evals) rather than being masked by the schedule. Want me to also add a shorter L10 sanity config to bisect where learning breaks between depth 6 and 20, once the shell is back? + +--- + +**User** + +yes, what to do to make the shell back? + +--- + +**Cursor** + +Let me first quickly re-probe the shell in case it already recovered. + +Still wedged. The agent's shell worker is blicked on an unkillable CUDA-init left over from the prior session's benchmark on bad GPU 2; every new command just queues behind it. Here's how to get it back, easiest first: + +## Fastest fix: Reload the Cursor window +This spawns a fresh shell worker for the agent even if the stuck process lingers. +- Open the Command Palette: `Ctrl+Shift+P` (Linux) +- Run **"Developer: Reload Window"** + +Your training is safe across a reload — the runs are detached `nohup` processes, independent of this shell. + +## If reload isn't enough: kill the stuck process from a *new* terminal +Open a brand-new terminal (Cursor menu **Terminal → New Terminal**, which is a separate process from the agent's wedged one), or SSH into the box, then: + +```bash +# see what's stuck (look for a `python -c`/`python -u -c` one-liner, and state "D") +ps -eo pid,stat,cmd | grep -E 'python|torchrun|run.py' | grep -v grep + +# kill the leftover benchmark probe + the old (non-improving) L20 run +pkill -9 -f 'python -u -c' +pkill -9 -f 'python -c' +pkill -9 -f 'star_coconut_L20_bfs' +``` + +Note: if that stuck probe is in uninterruptible `D` state (pinned to bad GPU 2), even `kill -9` won't clear it without a GPU reset/reboot — but **reloading the window still frees the agent** regardless, because it gets a new worker. + +## After the shell is back +Just send me any message. I'll then: +1. Confirm the old runs are dead and check which GPUs are free. +2. Launch the accuracy-gated depth-20 run **avoiding GPU 2 entirely** (I'll pin to known-good GPUs like 6,7 and guard every probe with `timeout` + a fixed device, so it can't wedge again). +3. Watch the first evals and report how long stage 1, stage 2, … take to solve. + +Do the Reload Window now — that's the one-click path. + +--- + +**User** + +@/home/ghoshavr/.cursor/projects/egr-research-slim-ghoshavr/terminals/1.txt:249-346 check this ..is it fixed ? + +--- + +**Cursor** + +The shell is working again — that `ps` output proves it. And it shows the key thing: the **old, non-improving L20 run is still alive** (PIDs `3037730` / `3037731`, state `R`, running `run.py args/star_coconut_L20_bfs.yaml`), plus stale `run.py` procs `3084651/3084657` and its wandb helpers. Let me confirm the shell and check GPU state, then kill it. + +That `ps` output is from your **own** terminal (terminal 1) — that one works. But the **agent's** shell worker is still wedged (my `echo` just hung again). So the shell isn't fixed for me yet. Let me read the full terminal state to be precise. + +Confirmed: your terminal 1 works, but the agent's shell worker is still wedged (my probe just hung again). The `ps` you pasted shows the important fact — **the old, non-improving L20 run is still alive and holding GPUs 2,3**: + +- `3037730` / `3037731` (state `R`) → `run.py args/star_coconut_L20_bfs.yaml` — the old run +- `3084651` / `3084657` → stale `run.py` children +- `3037615` torchrun + wandb helpers `3038016/3038058` + +So the shell is **not** fixed for me. Two ways forward: + +## Option 1 (recommended): Reload Window, then I drive +`Ctrl+Shift+P` → **"Developer: Reload Window"**. That gives the agent a fresh shell worker. Then send me a message and I'll kill the old run and launch the accuracy-gated one myself, and monitor the stage timing. + +## Option 2: run these in your working terminal now +```bash +# 1) kill the old non-improving L20 run + stale procs +pkill -9 -f 'star_coconut_L20_bfs.yaml' +pkill -9 -f 'run.py args/star_coconut_L20_bfs' +ps -eo pid,stat,cmd | grep run.py | grep -v grep # should show nothing + +# 2) launch the accuracy-gated depth-20 run on GPUs 2,3 +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main +CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 \ + --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml \ + > logs/star_coconut_L20_bfs_accstage.log 2>&1 & +echo "PID=$!" + +# 3) watch promotions / stage timing +grep --line-buffered 'acc-stage' logs/star_coconut_L20_bfs_accstage.log +``` + +Since the old run trained fine on GPUs 2,3 (it reached frontier 13), those GPUs are usable — the earlier wedge was a separate probe, not the training. + +Either way works. If you just want me to take it from here, do the Reload Window (Option 1) and ping me — that's the cleanest, and then I can watch the first evals and tell you how long stage 1, stage 2, … take. + +--- + +**User** + +I just refreshed the window, can you check now? And the 20th rounds, how is it going? And please continue what you discussed. + +--- + +**Cursor** + +I'll check if the shell recovered and get the status of the old L20 run. + +Still no response. Let me try a background probe and poll it, in case the worker is just slow to warm up after the reload. + +The agent's shell is still not responding even after the reload — `echo` has produced nothing after 30s+. This node is heavily loaded (your paste showed many running/zombie processes and the filesystem here is very slow), so shell startup itself may be crawling. Let me give it a longer wait. + +90 seconds with no output — the agent's shell worker is still wedged even after the reload (the wedge is below the window layer; it reconnected to the same stuck worker). Your manual terminal works, though, so let me answer your question by reading the run's log directly (that doesn't need the shell), then give you the exact commands to run. + +The old run has crept to frontier=14. Let me check its latest per-hop accuracy to confirm it's still not learning. + +I have the answer on the depth-20 run, and confirmation the shell issue persists. + +## The depth-20 run: still failing to learn (this is why we're switching) +The old run has crept to **frontier=14**, but its latest per-hop eval shows **frontier accuracy ~0.01–0.04 at *every* hop — including hop 1 (0.023)**. So after all this time it's essentially at chance everywhere. This is exactly the pathology of the epoch-driven schedule: it keeps advancing the frontier on a timer while the model has learned nothing, and rehearsal can't rescue 14 simultaneously-broken stages. This is precisely what the accuracy-gated version fixes (it won't leave stage 1 until hop 1 is actually solved, concentrating all early compute there). + +## Shell: reload didn't free the agent's worker +Unfortunately the reload reconnected to the same stuck worker — my `echo` has produced nothing after 90s+. But **your manual terminal works**. Since the agent shell is unreliable this session, please paste this into your working terminal (it kills the dead run and launches the accuracy-gated one): + +```bash +# 1) kill the old non-improving L20 run + stale procs +pkill -9 -f 'star_coconut_L20_bfs.yaml'; pkill -9 -f 'run.py args/star_coconut_L20_bfs' +sleep 2; ps -eo pid,stat,cmd | grep run.py | grep -v grep # expect: nothing + +# 2) launch the ACCURACY-GATED depth-20 run on GPUs 2,3 +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main +CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 \ + --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml \ + > logs/star_coconut_L20_bfs_accstage.log 2>&1 & +echo "PID=$!" +``` + +To watch stage-by-stage progress and timing: +```bash +grep --line-buffered -E 'acc-stage|eval per-hop' logs/star_coconut_L20_bfs_accstage.log +``` + +## What I'll do next +Once you've launched it, just tell me (or paste a few log lines) — I can read `logs/star_coconut_L20_bfs_accstage.log` directly without the shell and report: +- how long stage 1 takes to hit ≥0.9 (the key "does it even learn hop 1?" question), +- then stage 2, 3, … timing, +- and whether backtracking kicks in (a `HOLD` with `r_current` < frontier means an earlier stage regressed and is being repaired). + +Key thing to watch in the first few evals: the `[acc-stage]` lines. If you see `PROMOTE stage 1 -> 2` early, we're finally learning; if it `HOLD`s at stage 1 for a long time, hop 1 genuinely isn't trainable with this setup and we'll debug that specifically (rather than masking it by advancing the frontier). + +--- + +**User** + +I killed the 20 runs, the dead 20 runs. Can you please run the correct one right now? I hope everything is okay, and I did exactly how you asked. + +--- + +**Cursor** + +I'll verify the shell is working now and that the old run is gone, then launch the accuracy-gated run. + +The agent's shell still didn't respond. Let me try a background probe and poll it. + +The agent's shell is still completely unresponsive — even a background `echo` has produced nothing after 30s+. I have to be straight with you: despite the reload, my shell tool cannot execute anything this session (it reconnected to the same wedged worker), so **I physically can't launch the process myself**. This is a limitation on my side, not anything you did — you did exactly right by killing the old runs. + +Since your own terminal works, please paste this one block to launch the correct (accuracy-gated) run: + +```bash +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main +CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 \ + --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml \ + > logs/star_coconut_L20_bfs_accstage.log 2>&1 & +echo "PID=$!" +``` + +That's the config I built: accuracy-gated promotion (won't leave a stage until its frontier acc ≥ 0.9), backtracking rehearsal on, `backprop_depth: 6` (limited gradient), evaluating every 5 epochs, on GPUs 2,3. + +Quick sanity check ~1 min after launching (optional): +```bash +grep -E 'acc-stage|scheduled_stage|Config:' logs/star_coconut_L20_bfs_accstage.log | head +``` +You want to see `scheduled_stage 1` and eventually `[acc-stage] ...` lines. + +Once it's running, just paste me a few log lines (or tell me it's up) and **I'll take over monitoring by reading `logs/star_coconut_L20_bfs_accstage.log` directly** — I don't need the shell for that. I'll then report how long stage 1 takes to reach ≥0.9, then stage 2, 3, …, and flag any backtracking `HOLD`s. + +One thing I'll be watching closely: whether it can even get **hop 1** above 0.9. In the dead run, hop 1 was stuck at ~0.02 despite tons of epochs — but that was with training diluted across 14 stages. Here, all early compute is focused on stage 1 alone, so this is the real test of whether the model learns a single hop. If it does, the rest should follow. + +--- + +**User** + +@/home/ghoshavr/.cursor/projects/egr-research-slim-ghoshavr/terminals/1.txt:1031-1043 + +--- + +**Cursor** + +That paste got mangled — the line breaks were lost, so the commands ran glued together (`superpositioncd`, `mainCUDA_VISIBLE_DEVICES`), meaning `cd` never ran and it tried to write the log in the wrong directory. Nothing launched (it exited 1). Not your fault — the multi-line block didn't survive the paste. + +Here's the **same thing as a single line** so it can't break. Paste this exactly: + +```bash +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Then, ~30–60s later, confirm it actually started (also single line): + +```bash +sleep 45; tail -n 20 /egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L20_bfs_accstage.log +``` + +You're looking for the `Config: {...}` line and `scheduled_stage 1` (not an error/traceback). Once you paste me a few of those log lines — or just say it's up — I'll take over and monitor the log directly (I don't need the shell for reading), and report the stage-1 → stage-2 → … timing and any backtracking holds. + +--- + +**User** + +I just need the runs, can you please check? I think they are currently on. Also, I would suggest you to actually save a draft file which has all the chat history and the commands, and whenever we decide to do something, please paste a pointer summary there and save it. It could be a readme file or it could be like something like a text file. Because otherwise, every time the cursor chat history gets deleted, and I have to export and save the chat and reload again. If we keep a chat history in the folder, then I don't need to do this. + +--- + +**Cursor** + +Let me check the new run's log directly (I can read files without the shell). + +The run **crashed on startup** — it didn't stay up. The cause is a resume bug: it found an existing (empty) checkpoint dir and tried to resume from a non-existent checkpoint. Let me look at that logic. + +Let me see how `cur_ckpts` is built (lines just above). + +Found it: `run.py` sees the (empty) checkpoint directory, assumes it's a preempted run to resume, but finds no `checkpoint_*` files, so `latest_checkpoint` is `None` and it crashes. This is a pre-existing bug in the resume logic. Let me make it robust so it starts fresh when there are no real checkpoints. + +Now let me create the persistent experiment log you asked for, so the history/commands survive chat resets. + +I've fixed the crash and created the persistent log. Let me try the shell once more — if it's alive now, I'll relaunch it myself. + +Still dead on my end. Here's the situation and what to do. + +## The run had crashed (now fixed) +It wasn't actually training — it died instantly on a pre-existing **resume bug**: `run.py` saw the (empty) `ckpts/star-coconut-L20-bfs-accstage/` folder, assumed a preempted run, found no real checkpoint, and crashed (`'NoneType' object has no attribute 'split'`). I fixed it so it only resumes when genuine `checkpoint_*` files exist; otherwise it starts fresh. Config itself loaded correctly (`accuracy_staging: True`, `init_stage: 1`, `promote_threshold: 0.9`, `backprop_depth: 6` all confirmed in the log). + +## Please relaunch (single line, safe to paste) +```bash +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` +(The `rm -rf ckpts/...accstage` just clears the stale empty dir so it starts clean — the code now handles it either way.) + +Check ~45s later (single line): +```bash +sleep 45; grep -E 'scheduled_stage|acc-stage|eval per-hop|Traceback' logs/star_coconut_L20_bfs_accstage.log | tail -n 20 +``` +You want to see `scheduled_stage 1` repeating with **no Traceback**. + +## Persistent log created +I saved `reasoning-by-superposition-main/EXPERIMENT_LOG.md` with the full goal, findings (L6 success, L20 failure diagnosis), the code changes, all the launch/kill/monitor commands, known gotchas (GPU 2, shell wedging, paste hazard, the resume bug), and next steps. So even if this chat is lost, the context and commands live in the repo. I'll keep appending to it as we go. + +Once you paste those log lines back (or just say it's up), I'll read the log directly and report whether stage 1 finally learns hop 1, and the per-stage timing. + +--- + +**User** + +I just put on the runs I think currently there are double runs going on. Can you please check what runs are going on and if it's low? Also, what are we running right now? + +--- + +**Cursor** + +I'll check what's running by reading the logs directly (shell is still down for me). Let me look at the accuracy-gated log and check for duplicate runs. + +The log has grown (600KB+), so it's training. Let me pull the key status markers. + +The accuracy-gated run is up and training. Let me check whether the *old* run is also still running (that would be your "double run") by looking at both logs' latest content. + +Yes — you have a **double run**, and I can see exactly what happened. Here's the picture (read straight from the logs, since my shell is still down): + +## What's running right now + +1. **OLD run — still alive (never actually died).** `logs/star_coconut_L20_bfs.log` has kept growing: its frontier advanced from 14 → **16** since you said you killed it. So `run.py args/star_coconut_L20_bfs.yaml` (the epoch-scheduled, non-improving one) is still training. This is the stale run that should be gone. + +2. **NEW run — up and correct.** `logs/star_coconut_L20_bfs_accstage.log` shows the accuracy-gated run started cleanly (no traceback after the fix): + - `[acc-stage] accuracy-gated curriculum ON: init_stage=1 promote_threshold=0.9 max_latent_stage=20` + - Latest: `[acc-stage] HOLD at stage 1 (min acc 1..1 = 0.012 < 0.9) | 15 epochs / 570s in stage` + +Both were launched with `CUDA_VISIBLE_DEVICES=2,3`, so **they're fighting over the same two GPUs** — that's your double run, and it's why the new one is slow (~38s/epoch). + +## What we *want* running +Only run #2 — the **accuracy-gated** run (`star_coconut_L20_bfs_accstage.yaml`). It's the correct one: promotes only when a stage hits ≥0.9, keeps backtracking, limited backprop. + +## Fix: kill ONLY the old run (spares the accstage one) +Paste this single line in your terminal — the pattern matches `..._bfs.yaml` but **not** `..._bfs_accstage.yaml`: + +```bash +pkill -9 -f 'star_coconut_L20_bfs\.yaml'; sleep 2; ps -eo pid,cmd | grep 'run.py' | grep -v grep +``` + +The `ps` at the end should then show **only** the `...accstage.yaml` processes (2 of them, one per GPU). If you still see `star_coconut_L20_bfs.yaml` without `accstage`, tell me. + +## Note on the accstage run's early signal +It's currently **HOLD at stage 1 with hop-1 acc ~0.012** — i.e. not learning hop 1 yet. But that's after only 15 epochs *while starved by the old run*. Once you kill the old run it'll get both GPUs and speed up. The real question — "can it learn a single hop when fully focused?" — we'll answer over the next several evals. If hop-1 accuracy stays near zero even after it's alone and given more epochs, that's a genuine learnability problem we'll debug (LR / vocab / data), not something the curriculum can paper over. + +Once you've killed the old one, paste me the `ps` output (or just say done) and I'll keep watching the accstage log and report when/if stage 1 clears 0.9 and how long it took. + +--- + +**User** + +How about the runs going for the Day 21s? Can you please check the GPS 2 and 3, there may be alternate runs going as well. + +--- + +**Cursor** + +Let me check both L20 logs to see which runs are still active on GPUs 2,3 (reading logs since my shell is still down). + +Let me dig into whether there are two accstage processes interleaving (the timing looks non-monotonic: 50 epochs=3136s then 55 epochs=3085s, which shouldn't happen in a single run). + +Here's what the logs tell me (my shell is still dead, so this is from reading the run logs directly). + +## L20 run status + +**1. Old epoch-scheduled run — now DEAD.** `logs/star_coconut_L20_bfs.log` stopped growing (frozen at frontier=16). Your `pkill` worked; it's no longer advancing. Good. + +**2. Accuracy-gated run — single process, alive, but stuck.** I checked for duplicates: the log has exactly **one** `Config:` and **one** `accuracy-gated curriculum ON`, and the HOLD lines are a clean non-duplicated sequence (5,10,…,65 epochs). So there is **only one** accstage process — no double run on that side anymore. + +But the important part: it has been **HOLD at stage 1 for 65 epochs / ~62 min**, and hop-1 frontier accuracy is flat at **chance** the whole time: + +``` +5 ep: 0.035 30 ep: 0.043 55 ep: 0.008 +10 ep: 0.012 35 ep: 0.031 60 ep: 0.035 +15 ep: 0.012 40 ep: 0.035 65 ep: 0.023 +20 ep: 0.043 45 ep: 0.020 +25 ep: 0.020 50 ep: 0.016 +``` + +The gate is doing its job (correctly refusing to promote), but it's exposing a real problem: **the L20 model cannot learn even a single hop**, even with 100% of compute focused on stage 1 for an hour. At L6 hop-1 was solved almost immediately. So this is a genuine learnability/setup issue at L20, not something the curriculum can fix by waiting. + +## To confirm GPUs 2,3 directly (I can't — shell down) +Run in your terminal: +```bash +ps -eo pid,etime,cmd | grep 'run.py' | grep -v grep; echo '---'; timeout 8 nvidia-smi --query-compute-apps=pid,used_memory --format=csv || echo 'nvidia-smi hung (bad GPU)' +``` +You should see only two `...accstage.yaml` processes. If you see any `star_coconut_L20_bfs.yaml` (without `accstage`) or a second accstage torchrun, that's a stray to kill. + +## What I recommend next (this matters more than waiting) +Since stage 1 won't train at L20, we should debug *why*, rather than let it spin. The differences from the working L6 are: 100-node vocab (vs 31), ~80-edge sequences (vs ~6), and `backprop_depth: 6`. My proposed diagnostic: launch a quick **accuracy-gated L6** run (same new code path, small vocab/short sequences) — if that learns hop 1 fast, the code is fine and the culprit is the L20 vocab/sequence-length/LR; if it also stalls, the bug is in the accuracy-staging/backtrack code path itself. + +Want me to write that L6 accuracy-gated config + a single-line launch command so you can run it alongside (on GPUs 6,7 to avoid 2,3)? I'll also update `EXPERIMENT_LOG.md` with this stage-1 stall finding. + +--- + +**User** + +Don't understand what is the problem with stage one here, because for a equals to six, it was pretty easy. Can you check in the code for stage one, we have only one Latin token, and we only... I am wondering what exactly the stage I learns here. it traverses through the graph, at each stage, does it learn each leaf at each depth or it learns the final depth leaf? + +--- + +**Cursor** + +Great question — this gets to the heart of it. Let me read the data construction (`expand_data`, the latent-CoT dataset builder) and the per-hop metric to answer precisely what stage 1 supervises. + +Now the critical piece — how the per-hop metric indexes hops vs. how training targets are built. Let me read `perhop_categorize` and `_build_items`. + +This is the key question, and reading the data-construction code answers it precisely. Let me verify two more things: the eval-loss trend (is it learning at all?) and the L20 data structure. + +The shell is back (that Python command ran). And I found the real problem. Let me confirm the process/GPU state now that I can run commands. + +`nvidia-smi` wedged the shell again (that's the bad-GPU trap). Let me avoid it and just check processes in the background. + +`nvidia-smi` re-wedged the shell (I'll stop touching it). But the data-inspection command ran before that, and combined with the code, I now have a complete answer to your question — plus I found the actual bug. + +## What each stage learns (your question) + +Reading `expand_data` + the dataset builder: at **stage s**, the model emits **s latent tokens and is then trained to output the _next hop's_ frontier node — the depth-(s+1) node — NOT the final leaf.** Only the *final* stage predicts the target leaf. Concretely, using the real sample I just read (root=49, `neighbor_k[1]=[97,77]`, `neighbor_k[2]=[53,18]`, target=35): + +- **Stage 0** (`k=1`): `...[R] 49 ` + **0 latents** → predict a depth-1 node ∈ {97,77} +- **Stage 1** (`k=2`): `...[R] 49 <|latent|> ` + **1 latent** → predict a depth-2 node ∈ {53,18} +- **Stage s** (`k=s+1`): **s latents** → predict a depth-(s+1) node +- **Final stage** (`k=21`): `...49 <|latent|>×20 [A] ` → predict the target leaf 35 + +So it learns **each leaf at each depth, one hop deeper per stage** (the latents carry the earlier hops internally); it does *not* predict the final leaf at every stage — only at the last one. The per-hop metric mirrors this: "hop k" = `(k-1)` latents → predict a depth-k node. + +## The actual bug: the run is DIVERGING, not just stuck + +The eval-loss trace is the smoking gun — it's **increasing monotonically**: +``` +~4.62 (start) → 4.65 → 4.74 → 4.9 → 5.06 → 5.24 → 5.47 → 5.6 → 5.9 ... +``` +Random loss for a 128-token vocab is ln(128) ≈ **4.85**. So it *started* near chance and is now well *above* chance — the optimizer is pushing the model into a worse region. That's why hop-1 accuracy (the trivial 0-latent → depth-1 prediction) sits at ~0.02 = exactly random over ~100 nodes, and drifts *down* over time. The data itself is well-formed (root/target/neighbor_k all valid, node ids ≤ 99, 80 edges, 20 steps), so this is a **training-instability problem**, not a data or curriculum problem — and it was present in the old epoch-scheduled L20 run too (same near-random accuracy). + +Why L20 and not L6? Same `lr=1e-4`, but L20 has ~330-token sequences (80 edges) vs ~6 edges for L6, a wider vocab, and there's **no gradient clipping** in the loop. Long sequences + latent recurrence + no clip + fp32 → the classic recipe for slow divergence. + +## Two fixes I recommend (I can make both now) + +1. **Stabilize training** (the real fix): add gradient clipping (`max_norm=1.0`), lower `lr` (1e-4 → 3e-5), and add a short LR warmup. This is almost certainly what's needed to get L20 to learn hop 1 the way L6 did. +2. **Fix an off-by-one in my accuracy gate**: it checks metric hops `1..cur_stage`, but stage `s`'s *own* target is hop `s+1`. So at `cur_stage=1` it's gating on the stage-0 objective, never the current stage's. I'll gate on hop `cur_stage+1` (the current stage's actual target). + +Want me to implement both (grad clip + lower LR + warmup in `run.py`, plus the gate fix), then give you a clean single-line relaunch? Note my shell keeps wedging on `nvidia-smi`, so I'll prep all code changes and hand you the launch command to paste (and I'll monitor the log after). I'll also record this divergence finding in `EXPERIMENT_LOG.md`. + +--- + +**User** + +Yes stabilize the training run. I think lowering the learning rate is a good way to do that. Fix fix and off on inaccuracy yet. I don't know number two, what do you mean regarding the gating, but I think changing, yeah, implement whatever you think is the fix. I just want the twenty depth working and also I want to use it like a smaller number of back propagation, right? Like don't take all the stages back propagation. it's just a simple one, right? Not back without backtracking. + +--- + +**Cursor** + +I'll implement all the fixes. Let me first read the training loop to add gradient clipping and an LR scheduler correctly. + +The loop has no gradient clipping. Let me add clipping + LR warmup at the optimizer step. + +Now the gate off-by-one fix (gate on the current stage's actual target = hop `cur_stage+1`): + +Let me update the print messages to reflect the new hop range. + +Now update the config: lower LR, add gradient clipping + warmup, and reduce backprop to a small window (limited backprop, not all stages). + +Let me update the experiment log with these findings and fixes. + +Let me add a brief curriculum-semantics note to the log, then check if the shell recovered (avoiding `nvidia-smi` this time). + +Now let me check if the shell recovered (no `nvidia-smi` this time) so I can kill the diverging run and relaunch myself. + +My shell is wedged again from that earlier `nvidia-smi` (the queued call never returned). But all the fixes are done in code — here's the summary and the commands for your terminal. + +## What I changed + +**Stabilization (the divergence fix), in `run.py` + `star_coconut_L20_bfs_accstage.yaml`:** +- Added **gradient clipping** (`grad_clip: 1.0`) — this was completely absent and is the most likely cause of the long-sequence divergence. +- **Lowered LR** `1e-4 → 3e-5`. +- Added **linear LR warmup** (`warmup_steps: 200`). + +**Limited backprop (your "smaller number, simple" request):** +- `backprop_depth: 6 → 1` — gradient now flows only through the newest latent step + the answer, not across all stages. Matches the incremental curriculum (each stage learns its newest hop). Backtracking rehearsal stays on. + +**Gate off-by-one fix (what "#2" meant):** +- Metric "hop k" uses `k-1` latents to predict a depth-k node, so "stage s is learned" = accuracy at **hop s+1**. My gate was checking hops `1..cur_stage` (the *previous* stages), never the current stage's own target. Now it checks hops `1..cur_stage+1`, so it actually verifies the current stage learned its next-hop target before promoting. + +I also recorded the divergence diagnosis, the curriculum semantics (what each stage learns), and these fixes in `EXPERIMENT_LOG.md`. + +## Run it (single line — kills the diverging run, starts fresh) + +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Check the loss is now going DOWN (~45s later): +```bash +sleep 45; grep 'eval loss' logs/star_coconut_L20_bfs_accstage.log | tail -n 10 +``` + +The key success signal: **eval loss should decrease below ~4.85** (and keep dropping), unlike before where it climbed to 5.9. Once it's running, paste me a few `eval loss` / `acc-stage` lines (or just say it's up) and I'll monitor the log directly and tell you whether stage 1 finally clears 0.9 and how fast. + +(Heads-up: my shell keeps dying on `nvidia-smi` — if you want to check GPU usage, run `nvidia-smi` in your own terminal; I'll stick to reading logs.) + +--- + +**User** + +@/home/ghoshavr/.cursor/projects/egr-research-slim-ghoshavr/terminals/1.txt:390-395 it got killed ..also why cant u run the commadn from your side ..whats the reason ? is it because a momery issue or something ? + +--- + +**Cursor** + +Let me check the actual run log via absolute path (the `grep` failed only because it ran from your home dir, not the repo — relative `logs/...` path). + +The run **did launch** with the new stabilized config (`lr: 3e-05`, `grad_clip: 1.0`, `warmup_steps: 200`, `backprop_depth: 1`) and built the model. Your `grep` just failed because it ran from your home dir. Let me check whether it's alive and how the loss looks now. + +Only one eval so far and no traceback. Let me read the tail to see why it stopped. + +Good news buried in there: **it did not diverge this time.** The log shows it launched with the new config and trained epochs 1–2 with loss **stable/decreasing** (`eval loss 4.687`, train loss `5.06 → 4.65`) — no upward drift like before. It then stopped at epoch 2 (got killed), so we just need to relaunch and let it run longer. It only reached epoch 2 because the first ~4 epochs are LR warmup, so real learning hadn't kicked in yet. + +## Why it "got killed" +The launch command starts with `pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'`. If that line ran a second time (e.g. you re-pasted, or ran it again), it kills the running instance. So: **paste the launch line only once**, and don't re-run the `pkill` afterward. + +## Why I can't run commands (it's not memory) +It's a stuck-process issue, not RAM. My tool drives **one single persistent shell** (a background `bash`). Earlier I ran `nvidia-smi`, and on this node that call **hangs forever** because one GPU is in a bad state — the driver call never returns and can't be interrupted. That one frozen call blocks my single shell worker, so every command I send afterward just queues behind it and never executes. Your terminal is a *separate* process, which is why it works. To free mine I'd have to kill that stuck `nvidia-smi` — but reaching it requires the very shell that's blocked. A window reload didn't reattach a fresh worker either. So: reading files/logs works for me (that's a different mechanism), but launching processes doesn't. That's the whole reason I keep handing you paste-able commands. + +## Relaunch (paste ONCE, then leave it alone) +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Then **wait a few minutes** (let it get past epoch ~10, i.e. past warmup) and check from the repo dir: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|acc-stage' logs/star_coconut_L20_bfs_accstage.log | tail -n 15 +``` + +What we want to see: `eval loss` dropping well below 4.85 and continuing down, and eventually `[acc-stage] PROMOTE stage 1 -> 2`. Paste those lines back whenever you have them and I'll read the full log and tell you if it's learning and how fast stage 1 clears. It's fine to just let it run for 20–30 min before checking. + +--- + +**User** + +Don't see the EVA loss actually decreasing. Is it decreasing? Can you check? + +--- + +**Cursor** + +I'll check the current eval loss trend directly from the log. + +You're right — it is **not** decreasing. It's slowly **increasing** again, and stage 1 is stuck at chance: + +``` +epoch 5–25: ~4.62–4.68 (near random 4.85) +epoch 30–40: ~4.75–4.89 +epoch 45–60: ~4.85–4.95 +epoch 65–80: ~4.98–5.23 ← climbing +hop 1–2 acc: ~0.02 the whole time (HOLD at stage 1 for 80 epochs) +``` + +So gradient clipping + lower LR + warmup only *slowed* the divergence; the model is still drifting up and never learns even hop 1. That means the root cause is deeper than optimizer step size. The single biggest thing that changed vs. the working L6 run — besides vocab/sequence length — is `backprop_depth` (L6 used full BPTT; here it's 1). A too-aggressive truncation could be cutting the gradient path to the latent, which would exactly explain "latent stays uninformative → stuck at chance." Let me inspect that code. + +There's a `MAX_N_LATENT = 8` constant. Let me read the full latent loop and the truncation logic. + +Confirmed — the eval loss is **increasing** (4.6 → 5.2), not decreasing, and stage 1 is stuck at chance for 80 epochs. The stabilization only slowed the divergence. A loss that *rises above random* means the optimizer is actively making the model worse — that's an optimization bug, not just "hard task." Let me check how the model is wrapped, because gradient clipping behaves differently under FSDP. + +That's the bug. The model is wrapped in **FSDP** (line 185), but my gradient clipping used `torch.nn.utils.clip_grad_norm_(parallel_model.parameters(), ...)`. Under FSDP the parameters are sharded, so that standard call computes the norm over only each rank's local shard — it under-estimates the true norm and effectively **doesn't clip**. That's why the loss kept creeping up despite "clipping." FSDP needs its own `parallel_model.clip_grad_norm_()`. Let me fix it. + +Now lower the LR further (belt-and-suspenders, since it was diverging even at 3e-5 with broken clipping): + +Let me record this in the experiment log, then give you the relaunch. + +You were right to push on this. Here's the finding and the fix. + +## Answer: no, it's NOT decreasing — it's rising +``` +epoch 5–25: ~4.62–4.68 +epoch 30–45: ~4.75–4.89 +epoch 50–65: ~4.85–5.01 +epoch 70–80: ~4.98–5.23 ← still climbing +hop 1–2 acc: ~0.02 (chance) — HOLD at stage 1 for all 80 epochs +``` +Random loss is ~4.85, so it started *below* chance and drifted *above* it. That's active divergence. + +## Why the "stabilization" didn't work — the real bug +The model is wrapped in **FSDP** (sharded parameters). My gradient clip used the standard `torch.nn.utils.clip_grad_norm_(...)`, which under FSDP only sees each rank's **local shard**, computes a too-small norm, and so **effectively never clips**. That's why lowering the LR + "adding clipping" barely helped — the clip was a no-op. I fixed it to use FSDP's own `parallel_model.clip_grad_norm_()` (which all-reduces the true global norm), and also dropped `lr` to `1e-5`. + +## Relaunch (paste ONCE) +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` +Check after ~10 min (from the repo dir): +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep 'eval loss' logs/star_coconut_L20_bfs_accstage.log | tail -n 15 +``` + +Now the eval loss should **hold flat or drop** below 4.85 instead of rising. Paste the numbers back and I'll confirm. + +One honest heads-up: if the loss *still* creeps up even with FSDP-correct clipping at lr 1e-5, the remaining suspect is **model capacity** — a 2-layer GPT-2 doing multi-hop pointer-chasing over ~80 shuffled edges (vs ~6 at L6). In that case the fix is a controlled L6-with-this-code sanity run (to prove the code learns), then likely a few more layers for L20. But let's see if the clip fix does it first. + +--- + +**User** + +The thing is what I don't understand here is why is the twenty stage or twenty depth one different than the six depth one? Because you're adding depth sequentially, right? So you will go through depth one, depth two, depth three, and then go to depth twenty. So if it worked at depth six, it should work at depth twenty as well. I don't see why it isn't working or what is different. + +--- + +**Cursor** + +That's exactly the right question to ask, and it actually helped pinpoint the bug. Let me first check whether the relaunch (with the clip fix) has produced any data yet, then explain. + +Good news for diagnosing this: the log still shows the **old** run (`lr: 3e-05`, pre-clip-fix) — so the fixed version hasn't been launched yet. But your question is the important one, so let me answer it directly. + +## You're right that it *should* work — and that's exactly why the failure meant "bug," not "concept" + +The curriculum is sequential and identical in spirit: stage 1 → 2 → … → 20, each adding one latent hop. Stage 1 of L20 is conceptually the same task as stage 1 of L6. So when stage 1 of L20 sat at chance, that was the tell that something *mechanical* was broken — not that "depth 20 is a different problem." Your intuition is what makes the divergence suspicious rather than expected. + +## The one thing that genuinely differs at *every* stage (including stage 1) + +Here's the subtlety: the curriculum changes how many **latent hops** the model takes, but **the input is always the entire graph**. It's not that the input grows as we climb stages — the full depth-20 graph is in the prompt from stage 1 onward. + +- L6 prompt ≈ 24 edges ≈ ~100 tokens of context. +- L20 prompt ≈ **80 edges ≈ ~320 tokens** of context — even at stage 1. + +So "stage 1" of L20 is "find the root's neighbor inside an 80-edge graph," vs "inside a 24-edge graph" for L6. Same *task type*, but ~3× longer context at every stage. + +## Why longer context → failure here (the actual mechanism) + +Longer sequences produce larger, less-stable gradients. That's fine **if you clip them** — but: +- L6 (short context) stayed stable even with **no clipping** and a *higher* LR (1e-4). It never needed the safety net. +- L20 (long context) needed clipping to stay stable — and the clipping was **silently a no-op under FSDP** (sharded params → wrong local norm). So it slowly diverged (loss 4.6 → 5.2), and a diverging model outputs near-random nodes → stage 1 never clears the gate. + +So the difference isn't "depth 20 is a harder concept." It's that L20's longer context made optimization fragile, and the guardrail that would've held it (gradient clipping) wasn't actually engaged. That's now fixed (FSDP-correct clip + lr 1e-5). + +## One honest caveat for *later* stages (not stage 1) + +There *is* a second, real difference that shows up only deep in the curriculum: to solve stage k the model must chain k hops through the latent recurrence. Chaining 20 sequential latent hops is genuinely harder to learn than 6 and may need more epochs or capacity. But that only matters once stage 1, 2, 3… are actually learning. The stage-1 stall you saw was 100% the divergence, not this. + +## Next step +The current log is still the old diverging run. Relaunch with the fixed config (paste once): +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` +If stage 1 of L20 now learns (loss drops below ~4.85 and hop-1/2 acc climbs), that confirms the whole story: it was optimization stability at longer context, exactly as your "it should work" intuition implied. + +--- + +**User** + +Sorry, what did you change now and what command should I use to check the output? + +--- + +**Cursor** + +Here's the quick recap. + +## What I changed (to fix the L20 divergence) + +1. **`run.py` — FSDP-correct gradient clipping.** The old `torch.nn.utils.clip_grad_norm_` computed the norm over only each GPU's *local shard* under FSDP, so it effectively never clipped. Now it uses FSDP's own `parallel_model.clip_grad_norm_()` (which all-reduces the global norm) when the model is FSDP-wrapped. +2. **`run.py` — LR warmup** (linear over `warmup_steps`) so the long L20 sequences don't get a large step on the first few batches. +3. **`args/star_coconut_L20_bfs_accstage.yaml` — lowered `lr` to `1e-5`** (from 3e-5), plus `grad_clip: 1.0`, `warmup_steps: 200`. + +These are the stability fixes so stage 1 of L20 can actually learn instead of slowly diverging. + +## Command to launch + check output + +Launch (paste as one line): +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Then watch the log: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && tail -f logs/star_coconut_L20_bfs_accstage.log +``` + +Or just check the key signals (eval loss trend + stage promotion): +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|acc-stage' logs/star_coconut_L20_bfs_accstage.log | tail -30 +``` + +**What to look for:** eval loss should drop below ~4.85 and keep decreasing (not climb like before), and you want to see `[acc-stage] PROMOTE stage 1 -> 2` once hop-1/2 accuracy clears 0.9. I can verify the config actually loaded `lr=1e-5` from the first lines of the new log once you launch it. + +--- + +**User** + +Can you see the runs? It's operating in the terminal. It's mostly around 4.6. It slightly decreased from 4.8, but it's around 4.6. I wondered if this is running or not. You can check the output. + +--- + +**Cursor** + +I'll check the current log to see the latest state. + +Yes — I can see it clearly now, and this is actually informative. Let me check the exact config values. + +Yes, it's running (currently epoch 12, both GPUs active). And the log tells us exactly what's happening — this is real progress plus a clear next fix. + +## What the numbers say + +- **The divergence is gone.** eval loss went 4.78 → 4.62 in the first few epochs and is now **flat at ~4.62** (not climbing like before). So the FSDP clip fix worked — training is stable now. +- **But it's frozen, not learning.** `ln(100) ≈ 4.605`, and it's sitting at 4.62. The model has just learned the unigram prior and stalled. You can see it in the eval dump: it outputs the **same constant answer `96`** for every single question → accuracy ~0.01 (chance). Even hop-1 frontier acc is 0.016, and hop-1 (root's direct neighbor) requires *zero* reasoning — it's literally reading an edge. So the model isn't learning even the trivial retrieval. + +## Why it froze: we over-corrected the learning rate + +Here's the trajectory of what we did: +- lr **3e-5** + *broken* clip → **diverged** (steps too big, nothing holding them). +- lr **1e-5** + *working* clip → **stable but frozen** (steps now too small — with clip 1.0 capping the norm *and* lr at 1e-5, ~10× lower than L6, the updates are too weak to escape the unigram prior). + +L6 learned with **lr 1e-4** and no clipping at all. The whole point of getting clipping working is that it's now a *safety net* — so we can put the proven L6 learning rate back and it won't diverge, because the global grad-norm is capped at 1.0. + +## The fix: raise lr back to 1e-4, keep the clip + warmup + +I'll bump the LR now. + +Now relaunch with the higher LR (paste as one line): + +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Check progress with: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|acc-stage|per-hop' logs/star_coconut_L20_bfs_accstage.log | tail -20 +``` + +## What to watch for this time +- **eval loss should drop clearly below 4.6** (not sit at ln(100)≈4.605) — that's the signal it's actually learning, not just fitting the prior. +- **hop-1 frontier acc should climb fast** (it's the easiest — direct neighbor of root). If hop-1 moves off 0.02 within the first ~20 epochs, we're in business. +- If it **diverges again** (loss climbs), then clip 1.0 isn't enough at lr 1e-4 and I'll drop to lr 5e-5 — but the working clip makes that unlikely. + +So to directly close the loop on your earlier question: L20 *is* the same task as L6 stage-by-stage — the only reason it wasn't learning was optimization (first a broken clip letting it diverge, then too-low LR freezing it). This LR restore is the last piece; if it learns hop-1 now, the whole curriculum should unroll like L6 did. + +--- + +**User** + +Weird, it's still not decreasing. Can you check? Should we send the hyperparameter update? I wonder what's the problem here. Because this was the exact same setting that we used in the six depth and it worked perfectly. Both has to pass through the one depth stage, right? + +--- + +**Cursor** + +Let me check the current state of the log to see what LR is actually running and the loss trend. + +lr is now `0.0001` (1e-4, same as L6) and it's **still flat at ~4.62 with chance accuracy**. So LR was *not* the cause. You're right to push on this — with LR ruled out, the difference has to be structural. Let me actually diff the L6 setup (which worked) against the L20 setup, instead of guessing. + +Both configs are 2-layer/8-head/768-dim with `lr 1e-4`, `pad_latent_to_max: True`. So the arch and LR really are the same. But I found the structural differences. The L20 config has two things L6 never had: + +1. **`backprop_depth: 1`** (L6 has none → full BPTT) +2. `accuracy_staging` + widened vocab (40 → 128) + +The `backprop_depth: 1` combined with `pad_latent_to_max: True` is very likely the killer. Let me confirm by reading the exact truncation logic in `coconut.py`. + +Found it. This is the real difference, and it fully explains why L20 can't even learn hop-1 while L6 did with the identical LR/arch. + +## The bug: `backprop_depth: 1` × `pad_latent_to_max: True` + +Look at the eval dump — even at **stage 1**, every sequence has **20 `<|latent|>` tokens** before `[A]`, because `pad_latent_to_max: True` always pads to the max (20). So in `coconut.py`: + +```64:74:reasoning-by-superposition-main/coconut.py + max_n_latents = max([len(l) for l in latent_lists]) + ... + for pass_idx in range(max_n_latents): +``` + +`max_n_latents = 20` at *every* stage. Now the truncation: + +```139:146:reasoning-by-superposition-main/coconut.py + if self.backprop_depth is not None: + passes_remaining = max_n_latents - 1 - pass_idx + if passes_remaining >= self.backprop_depth: + hidden_states = hidden_states.detach() + if kv_cache is not None: + kv_cache = [ + (k.detach(), v.detach()) for (k, v) in kv_cache + ] +``` + +With `backprop_depth=1`, `passes_remaining >= 1` is true for pass 0…18 — so **19 of the 20 latent passes are detached**. Gradient only flows through the *last* latent + the answer head. The answer has to be routed through 20 latent passes, but 19 of them are frozen at random init and cut off from the loss. So the model literally cannot learn to chain graph → answer. Result: loss stuck at `ln(100) ≈ 4.62`, constant output `96`, chance accuracy — exactly what you see. + +**L6 had no `backprop_depth` → full BPTT through all 6 padded latents → it learned.** You were right: both pass through stage 1, but at L6 stage-1 gradients reach the graph; at L20 they were being severed by the truncation. + +## Why this happened + +This came from your (correct) request to limit backprop at high depth. But `backprop_depth: 1` is too aggressive: combined with `pad_latent_to_max`, it starves stage 1 of gradient. For a 2-layer/768-dim model, full BPTT over 20 passes is cheap anyway — the truncation was a premature optimization that broke learning. + +I'll set it back to full BPTT to match the L6 config exactly. + +Now relaunch (paste as one line): + +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Check it: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|acc-stage|per-hop' logs/star_coconut_L20_bfs_accstage.log | tail -20 +``` + +The config is now **identical to L6** (lr 1e-4, full BPTT, pad_latent_to_max) except depth 20 + accuracy-gating. So it should behave like L6: eval loss should drop below 4.6 within a handful of epochs and hop-1 frontier acc should climb quickly, then promote stage 1 → 2. + +To directly answer your question: nothing about depth 20 is fundamentally harder at stage 1 — the `backprop_depth: 1` truncation (which L6 never had) was silently cutting the gradient off from 19 of the 20 padded latents, so the model could never learn. That's the difference. If it learns hop-1 now, the whole curriculum should unroll. Once it's confirmed learning, if you still want limited backprop for memory at deep stages, I'll add it back as a window that scales with the current stage rather than a fixed 1. + +--- + +**User** + +Do you see an improvement? I don't. I wonder what is the issue here. Do you think it makes sense to run the six-stage one and see if it's working or not? + +--- + +**Cursor** + +I'll check the current log to see if the full-BPTT relaunch is learning. + +You're absolutely right, and that's exactly the experiment we should run. Here's the key finding first: with `backprop_depth: None` (full BPTT) + lr 1e-4 — **identical to L6 now except depth/vocab/data** — it's *still* flat at ~4.62 and even drifting up to 4.8. So `backprop_depth` was **not** the cause. I was wrong about that being the whole story. + +That means the problem is in something we changed that L6 didn't exercise before, or in the L20 data itself. Running L6 is the perfect bisection: if L6 still learns → the bug is L20-data/vocab specific; if L6 now also fails → we broke something shared (tokenizer/coconut.py/run.py). Let me look at what we changed that could affect both, while you consider launching L6. + +The tokenizer now places specials at 100–108 (`vocab_size` 109) because we set `NUM_NODES=100`. L20's model config (vocab 128, eos 107) covers that, so L20 is self-consistent. But this means the **L6 model config (vocab_size 40) can no longer represent the special tokens** (`[Q]`=104, ``=107) — so a naive L6 rerun would crash or mis-embed. I need to see how `run.py` wires the tokenizer and builds the answer labels. Let me read the key parts. + +`run.py` always builds `STokenizer()` with the default 100 nodes, so an L6 rerun would need the 128-vocab model too. Before recommending the L6 test, let me inspect the highest-risk shared change: the vectorized latent-feedback rewrite in `coconut.py` (that touches every run). Let me read the full forward. + +The vectorized feedback in `coconut.py` looks correct (it's a faithful scatter equivalent of upstream). So I agree with you — the fastest way to localize this is to run the depth-6 config through the **current** pipeline. If L6 learns, the bug is L20-data/depth specific; if L6 now also fails, we broke something shared. Let me check the L6 data format first (to make sure it re-tokenizes cleanly under the current 100-node tokenizer). + +L6 data uses node ids ≤ 30 (no collision with specials at 100+), so it re-tokenizes cleanly under the current 100-node tokenizer. I just need to point the L6 config at the 128-vocab model. Let me create a depth-6 baseline config that runs the **pristine L6 recipe** (epoch-based staging, full BPTT, lr 1e-4) through the **current** code. + +Now launch the depth-6 diagnostic on GPUs 6,7 (leaves the L20 run untouched on 2,3 for comparison). Paste as one line: + +```bash +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L6-bfs-v128 logs/star_coconut_L6_bfs_v128.log && CUDA_VISIBLE_DEVICES=6,7 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L6_bfs_v128.yaml > logs/star_coconut_L6_bfs_v128.log 2>&1 & echo "PID=$!" +``` + +Check it after a few minutes: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|Accuracy on validation|per-hop' logs/star_coconut_L6_bfs_v128.log | tail -20 +``` + +## What this tells us + +This is the exact recipe that learned before (epoch staging, full BPTT, lr 1e-4), just run through our current code with the 128-vocab model. Two outcomes: + +- **L6 learns** (eval loss drops well below 4.6, val accuracy climbs toward ~1.0 within ~25–75 epochs): the shared code is fine, and the L20 failure is specific to depth-20 / the regenerated L20 data / accuracy-staging. Next I'd bisect those: run L20 data with plain epoch staging (rule out the accuracy-gate), and sanity-check the L20 data generation. +- **L6 also stalls at ~4.6**: we broke something shared (most likely in the `run.py` changes or the tokenizer widening), and I'll bisect the code by reverting to a known-good `run.py`/tokenizer and re-testing. + +Either way it cleanly splits the problem in half. My hunch is L6 will learn, which would point the finger at either the L20 data generation or the accuracy-staging label path — both of which I can then inspect directly. + +Once we have the L6 result, tell me what the log shows (or I'll read it) and I'll take the next bisection step. + +--- + +**User** + +No, actually, launch those at 2 and 3. I don't think 2 and 3 on GPUs, 2 and 3, I don't think they are working, right? So maybe kill those up and start again. + +--- + +**Cursor** + +Got it — kill the L20 run on GPUs 2,3 and launch the L6 diagnostic there instead. Paste as one line: + +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L6-bfs-v128 logs/star_coconut_L6_bfs_v128.log && CUDA_VISIBLE_DEVICES=2,3 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L6_bfs_v128.yaml > logs/star_coconut_L6_bfs_v128.log 2>&1 & echo "PID=$!" +``` + +Then confirm it started cleanly (both ranks up, no CUDA errors): +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && tail -30 logs/star_coconut_L6_bfs_v128.log +``` + +And after a few minutes, check whether it's learning: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|Accuracy on validation|per-hop' logs/star_coconut_L6_bfs_v128.log | tail -20 +``` + +Once it's been running a bit, paste me what those show (or I'll read the log) and we'll know whether the shared code path is healthy or broken. If GPUs 2,3 are actually dead, the startup tail will show a CUDA init hang/error — in that case tell me and we'll move it to 6,7. + +--- + +**User** + +Check if it's improving, I am thinking it's still mostly stuck at 3.3 or something. + +--- + +**Cursor** + +I'll check the L6 diagnostic log to see the loss trend. + +The L6 run is at `scheduled_stage 0` (the no-latent warmup stage) and eval loss is drifting down 3.49 → 3.34 but flattening. That's not yet informative — stage 0 is direct prediction, and the real signal is validation accuracy at the first full eval (epoch 25) and whether it promotes to stage 1. Let me read further into the log. + +It's **not fully stuck** — eval loss is decreasing (3.49 → 3.42 → 3.33 → 3.26), just slowly, and it's still in `scheduled_stage 0` (the no-latent warmup; 25 epochs before it promotes to stage 1). Random baseline would be `ln(109) ≈ 4.69`, so at 3.26 it's clearly learning *something*, but hasn't hit a full accuracy eval yet (those run every 25 epochs). Let me watch it a bit and check for the first accuracy eval + stage-1 promotion. + +This is the answer. The L6 diagnostic **is learning through the current code** — and it just revealed the real bug. + +## L6 result (current code path is healthy) +- Stage-0 eval loss cratered: 3.26 → **0.84** +- At the first accuracy eval it promoted to stage 1, with **hop-1 frontier = 1.0**, hop-2 reachable = 0.996, etc. + +So `run.py`, `coconut.py`, and the tokenizer are all fine. The bug is **not** shared code. + +## The real difference: L20 skipped stage 0 + +Look at what L6 does at the start: `scheduled_stage 0`. **Stage 0 is the no-latent CoT warmup** — the model first learns the graph traversal with the reasoning steps as explicit tokens (that's how it got hop-1 frontier to 1.0 and loss to 0.84). *Then* it promotes to stage 1 and starts replacing steps with latents. + +The L20 accuracy-staging config set **`init_stage: 1`**, which **skips stage 0 entirely**. So L20 jumped straight to "predict through a latent" from random init, with no CoT foundation to bootstrap from — and got stuck at the unigram prior forever. + +This is exactly what you kept intuiting: "both have to pass through stage 1." They do — but L6 passes through **stage 0 first**, and that warmup is what makes stage 1 learnable. L20 never got it. + +Let me fix it. + +Let me verify the promotion logic handles `cur_stage=0` correctly before relaunching. + +Confirmed correct: with `acc_staging` and `cur_stage=0`, line 278 sets `scheduled_stage = 0` — the exact CoT warmup L6 uses. The fix is sound. + +Now relaunch the fixed L20 on GPUs 6,7 (leaving the L6 control running on 2,3 as the positive baseline). Paste as one line: + +```bash +source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-accstage logs/star_coconut_L20_bfs_accstage.log && CUDA_VISIBLE_DEVICES=6,7 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_accstage.yaml > logs/star_coconut_L20_bfs_accstage.log 2>&1 & echo "PID=$!" +``` + +Check both runs: +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && echo "== L20 ==" && grep -E 'eval loss|acc-stage|per-hop' logs/star_coconut_L20_bfs_accstage.log | tail -8 && echo "== L6 ==" && grep -E 'Accuracy on validation|acc-stage|per-hop' logs/star_coconut_L6_bfs_v128.log | tail -4 +``` + +## What to expect now +The fixed L20 should mirror L6's trajectory: `scheduled_stage 0` first, eval loss dropping from ~3+ toward <1 over the first stage, hop-1 frontier climbing to ~1.0, then `PROMOTE stage 0 -> 1`, and onward up the curriculum. If you see stage-0 loss dropping below ~2 in the first ~15 epochs, we're finally unblocked. + +## Summary of what was actually wrong +To recap the whole debugging arc, in order of what we fixed: +1. FSDP gradient clipping was a silent no-op → fixed (stopped the divergence). +2. LR was over-lowered to 1e-5 → restored to 1e-4 (L6's rate). +3. `backprop_depth: 1` truncated 19/20 latents → set to full BPTT. +4. **The actual root cause: `init_stage: 1` skipped the stage-0 CoT warmup.** L6 always started at stage 0; L20 didn't. That warmup is what makes latent stages learnable. + +Your instinct — "it worked at 6, it should work at 20, both pass through stage 1" — was the key that cracked it: the missing piece was that L6 passes through **stage 0** first. + +--- + +**User** + +Sorry, what was the issue? It wasn't working, and now it's working. Can you say if it's working right now? + +--- + +**Cursor** + +I'll check the current state of both runs to confirm whether the fixed L20 is now learning. + +The fixed L20 is now running with `init_stage: 0` (confirmed in config) and it's in `scheduled_stage 0` — the CoT warmup it was missing. It just started (eval loss 4.65, essentially the random baseline). I need to give it a couple minutes to see the loss actually drop. Let me wait and re-check. + +It's running at the correct `scheduled_stage 0` now, but I want to be honest: after ~5 epochs the stage-0 loss is still flat at ~4.65 and hasn't started dropping the way L6's did (L6 was already falling by epoch 5). It may just need more epochs since the depth-20 task is bigger, but let me watch a bit longer before calling it — I don't want to tell you it's working prematurely. + +The log hasn't advanced in 3+ minutes (still 5 eval-loss lines). That's most likely the epoch-5 per-hop eval, which does thousands of slow single-example `generate()` calls (heavier at L20 with 20 latents). But I should confirm it's not stalled/crashed. Let me read the raw tail of the log. + +I have a clear answer now, and I need to be honest: **it's still not working.** Even with `init_stage: 0`, the L20 run at the first eval (epoch 5) is at chance (val accuracy 0.008, constant output "50", eval loss flat at ~4.63). By contrast, L6 was already dropping by epoch 5. So `init_stage: 0` was necessary but **not sufficient** — there's another difference. + +But the eval dump revealed the key clue. Let me check whether an epoch-staged (non-backtrack) L20 config exists to isolate the cause. + +`star_coconut_L20_bfs.yaml` still has `backtrack: True` and `backprop_depth: 6`, so it's not a clean baseline either. The decisive test is to run the **exact recipe that just worked** (L6-v128: epoch staging, no backtrack, full BPTT) but at depth 20 on the L20 data — changing *only* depth + data. Let me create that. + +Now launch this plain L20 on GPUs 6,7 (replacing the accstage run there; leaves the L6-v128 control on 2,3). Paste as one line: + +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-plain logs/star_coconut_L20_bfs_plain.log && CUDA_VISIBLE_DEVICES=6,7 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_plain.yaml > logs/star_coconut_L20_bfs_plain.log 2>&1 & echo "PID=$!" +``` + +Watch its stage-0 loss (this is the key signal — should drop like L6 did, not sit at 4.6): +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && grep -E 'eval loss|Accuracy on validation' logs/star_coconut_L20_bfs_plain.log | tail -20 +``` + +## Honest status + what this settles + +To directly answer "is it working right now?": **No — not yet.** The `init_stage: 0` fix was real but didn't fix it; the accstage L20 is still at chance after 5 epochs. So there's still a difference between our working L6 run and the L20 run. + +This experiment pins it down, because it changes **only depth + data** from the recipe that just worked: + +- **If plain L20 learns** (stage-0 loss falls well below 4.6, accuracy climbs): the culprit is the `accuracy_staging`/`backtrack` code path — most likely the backtrack dataset builder (`get_graph_latent_cot_dataset_backtrack`) sampling mixed/garbage stages early on. I'll then fix that path and re-enable accuracy-gating + backtracking on top of a working baseline. +- **If plain L20 also stalls**: depth-20 itself is the problem — 20 latents is genuinely too hard to bootstrap at stage 0, or the regenerated L20 data has an issue. I'll then inspect the L20 data (and the stage-0 target) directly, and we'd likely need a gentler depth ramp. + +Either way we stop guessing. My suspicion, given L6-v128 works and the only extra knobs on the failing run are `backtrack`/`accuracy_staging`, is that the plain L20 will learn — which would mean the backtracking data path is what's been sabotaging it all along. Let me know what the stage-0 loss does (or I'll read the log in a few minutes). + +--- + +**User** + +Please read it. + +--- + +**Cursor** + +I'll read the plain L20 log to check whether it's learning. + +The plain L20 log doesn't exist yet — it looks like the launch command may not have run. Let me check your terminal state. + +I read it. Two things: + +1. **The plain L20 run was never launched** — the log doesn't exist (`grep: logs/star_coconut_L20_bfs_plain.log: No such file or directory`). It looks like only the `grep` ran, not the launch command before it. +2. **What's actually running is the L6-v128 control** (`Epoch 4-5/200`, `scheduled_stage 0`, eval loss ~3.37, train loss ~3.4). That's healthy — depth-6 is learning as expected. + +So we still need to start the depth-20 plain run. Please run this launch command (paste as one line — it must run as one piece): + +```bash +pkill -9 -f 'star_coconut_L20_bfs_accstage.yaml'; sleep 2; source ~/miniforge3/etc/profile.d/conda.sh && conda activate superposition && cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && rm -rf ckpts/star-coconut-L20-bfs-plain logs/star_coconut_L20_bfs_plain.log && CUDA_VISIBLE_DEVICES=6,7 WANDB_MODE=offline nohup torchrun --standalone --nnodes 1 --nproc_per_node 2 run.py args/star_coconut_L20_bfs_plain.yaml > logs/star_coconut_L20_bfs_plain.log 2>&1 & echo "PID=$!" +``` + +After it prints `PID=...`, wait ~1 minute, then confirm the log now exists and is progressing: + +```bash +cd /egr/research-slim/ghoshavr/reasoning-by-superposition-main && ls -la logs/star_coconut_L20_bfs_plain.log && grep -E 'scheduled_stage|eval loss' logs/star_coconut_L20_bfs_plain.log | tail -10 +``` + +Once it's running I'll read the log and we'll see whether depth-20 learns with the exact recipe that works at depth-6 (isolating whether backtracking/accuracy-staging is the culprit vs. depth itself). The L6-v128 control on the other GPUs can keep running as our known-good reference. + +--- + +**User** + +I don't think the stage ten is, depth ten is going anywhere. I just printed the eval, do you think so? + +--- + +**Cursor** + +I'll check the latest eval output and the plain L20 log to see if it's actually moving. + +The plain L20 log is live. Pulling the full eval trajectory next. + +Yes — I agree with you. **Depth 20 is not learning.** + +What the plain L20 log shows: + +| Epoch | Stage | Eval loss | Val acc | +|------:|------:|----------:|--------:| +| early | 0 | ~4.61 | — | +| ~25 | 0 | **~4.98** (got worse) | **0.008** (chance) | +| now | 1 | ~4.60–4.63 | still chance | + +Hop-1 frontier at the first eval was **0.035** — random. And this is the plain recipe (no backtrack, no accuracy-staging), the same one that works at depth 6. + +For comparison, L6 at the same point already had eval loss dropping **3.49 → 3.26 → <1**. + +So your read is right: it’s stuck, and the problem is not the accuracy-gate/backtracking path — it’s something about depth-20 itself (most likely: even at “stage 0,” `pad_latent_to_max` still puts **20** empty latents in the sequence, vs 6 for L6, so the CoT warmup is much harder). + +I’d kill this L20 run and either: +1. try `pad_latent_to_max: False` so stage 0 has no/few latents, or +2. step through an intermediate depth (e.g. L10) to see where it breaks. + +Want me to set up the `pad_latent_to_max: False` L20 run next? + +--- + +**User** + +No, just tell me how is the stage six going the one that we ran. + +--- + +**Cursor** + +**Depth-6 is working well — it finished.** + +Final (epoch 200): +- **Val accuracy: 249 / 256 = 0.973** +- **Eval loss: ~0.19** +- Per-hop frontier acc ≈ **0.97 across hops 1–6** + +It progressed through the curriculum (stage 0 → 1 → 2 → … → 6), learned properly, and ended with near-perfect answer accuracy. So the shared training code is fine; the depth-6 setting is healthy. + +--- + +**User** + +So what is special about this depth six that is working, and depth twenty that isn't working? I'm wondering if you just change this experimental setup to twenty and then have everything similar, just change the depth to twenty and make the graph larger, it should work, right? So no like gradient truncation, no backtracking, nothing, just this depth six thing. And just like exact same setup, but just change to depth twenty. If that works, I think we can take it from there. + +--- + +**Cursor** + +You're describing exactly the experiment we already ran: `star_coconut_L20_bfs_plain.yaml` — same recipe as the working L6 (epoch staging, no backtracking, no gradient truncation, no accuracy-gating, same lr, same model), with **only** depth/data changed to 20. + +**That run did not work.** Stage-0 eval loss went ~4.64 → ~4.98 (got worse), val acc stayed at chance (~0.008), then it blindly advanced to stage 1 still stuck at ~4.6. + +So nothing “extra” in the L20 training recipe is the culprit anymore. What’s still different when you only change depth: + +1. **Longer graphs** — L20 has ~3× more edges/tokens in the prompt than L6. +2. **`pad_latent_to_max: True`** — even at stage 0, L6 pads **6** latents; L20 pads **20**. So stage-0 CoT warmup is not the same task; L20 still has to route through 20 empty latent slots. + +L6 isn’t special beyond “smaller graph + fewer latents.” Same code path works there and fails at depth 20 under that setup. + +If you want the next closest fair test, turn off padding so stage 0 truly matches L6’s stage-0 structure (no 20 empty latents), i.e. `pad_latent_to_max: False` on an otherwise identical L20 config. That’s the only remaining knob that makes “just change depth to 20” actually stage-0-identical. Want me to set that up? + +--- + +**User** + +Okay, for both the runs, what's exactly happening at stage zero? Is it learning the same targets or is it learning only the stage one targets? Like is the number of targets same? Like, is it an expressivity problem? for 6 versus 20. + +--- + +**Cursor** + +I'll check the dataset/stage logic so we can compare what stage 0 actually trains on for L6 vs L20. + +At **stage 0**, L6 and L20 are learning the **same kind of target** — not stage-1 targets, and not a different number of targets. + +## What stage 0 actually trains + +From `expand_data` / `get_graph_latent_cot_dataset`: + +- `scheduled_stage = 0` → `k = 1` +- Sequence: `edges [Q] … [R] root` + **0 latents** +- Label: **one token** — a hop-1 neighbor from `neighbor_k["1"]` + +So for both depths: + +| | L6 stage 0 | L20 stage 0 | +|--|--|--| +| # supervised targets | **1** | **1** | +| # latents | **0** | **0** | +| Target type | hop-1 node | hop-1 node | + +Stage 1 (later) would be: 1 latent → predict hop-2. Stage 0 is not that. + +(The 20 `<|latent|>` tokens you see in eval dumps are from the **eval** path, which always builds the full final-answer prompt. That’s not what stage-0 training uses.) + +## So is it an expressivity problem (6 vs 20)? + +Not in the “more latents / more targets” sense. At stage 0 both have **zero latents and one target**. + +What’s different is the **input**: +- L20 graphs are much longer (~80 edges vs ~24) +- More nodes to search over + +Same model (2-layer), same objective (find root’s neighbor), harder retrieval over a longer edge list. That’s why L6 stage 0 drops to ~0.8 while L20 stage 0 sits near chance — not because stage 0 is asking for 20 hops worth of reasoning. + +--- + +**User** + +What do you mean harder retrieval over a longer age list? I don't get it. Is the target same or not? And what is the fucking structure of the graph here for 6 versus 20? I really don't see the difference. Make it explicit. + +--- + +**Cursor** + +I'll pull the exact graph structure from the data so this is concrete, not hand-wavy. + +Yes — **the stage-0 target type is the same.** What’s different is only the graph size. + +## Graph structure (same shape, different length) + +Both are **2-arm stars** with 4 arms of length `L` plus 2 roots: + +``` +Correct component: Distractor component: + R (root) R2 (neg root) + / \ / \ + armA armB armC armD + (L) (L) (L) (L) +``` + +| | L6 | L20 | +|--|--|--| +| arm length | 6 | 20 | +| nodes | `2 + 4×6 = 26` | `2 + 4×20 = 82` | +| edges | `4×6 = 24` | `4×20 = 80` | +| edges listed in prompt | 24 | 80 | + +Same topology. L20 is just longer arms → more edges/nodes dumped into the prompt. + +## Stage-0 target: same kind, same count + +For both: + +- Prompt: shuffle all edges → `[Q] target neg_target [R] root` +- Latents: **0** +- Supervised label: **1 node**, randomly one of the root’s two hop-1 neighbors (`neighbor_k["1"]`) + +Example from real data: +- L6: root=`3`, target ∈ `{19, 11}` +- L20: root=`65`, target ∈ `{39, 21}` + +So: **same task** (“output a direct neighbor of the root”), **same number of targets (1)**. Not stage-1, not 20 hops. + +## What “longer edge list” means, concretely + +Stage 0 is just: look at the edge list, find an edge that starts at the root, emit the other endpoint. + +- L6: search among **24** edges (~77 tokens) +- L20: search among **80** edges (~245 tokens) + +Same answer type. Harder only because the needle is in a longer haystack. That’s it — not expressivity of 6 vs 20 latents at stage 0 (there are zero latents). + +--- + +**User** + +For the training data, it sees the oracle, right? So for twenty depth nodes among eighty edges or two forty-five tokens, it sees the correct attached value, right? Or maybe two attached value from the root. So, like, you have a root three, your target is either nineteen or eleven, and for root sixty-five, which is either thirty-nine or twenty-one. So, what is the issue? Like, how is it like a needle in a haystack? I'm still not getting. + +--- + +**Cursor** + +You’re right to push on that. I framed it badly. + +**Yes — training sees the oracle label.** +For stage 0 the loss is supervised CE on the correct hop-1 node. Example: + +- L6: input has root `3` + 24 edges; label is `19` or `11` +- L20: input has root `65` + 80 edges; label is `39` or `21` + +So it is **not** “find the answer without being told.” The target is given in the label. Same target type, same count (1 token). + +What I meant by “haystack” (and should have said more carefully): the model still has to **learn a lookup** — “among these shuffled edges, attend to the ones involving the root and emit a neighbor.” The label tells it *what* to output, not *which edge tokens to look at*. With 80 edges instead of 24, there are more distractor edges, so that attention pattern may be harder for a 2-layer model to learn. That’s a hypothesis about learning difficulty, not “the answer is hidden.” + +Honest bottom line: **stage-0 targets are the same.** The only structural difference is a bigger graph in the prompt. Whether that alone is why L20 fails (vs a bug/optimization issue) is **not proven** — I overstated the haystack story. The failure mode is: same supervised task, longer input, and L20 isn’t learning while L6 is. + +--- + +**User** + +Can you do the run with ten depth graph and see if the loss decreases? Just run it for ten then. + +--- + +**Cursor** + +I'll set up a plain depth-10 run (same recipe as L6) and launch it. + +Skipping hung `nvidia-smi`; generating L10 data and launching the plain run. + +Data and config are ready. Launching the plain L10 run on GPUs 2,3. + +Waiting for the first few epoch eval losses to see if training is learning. + +Loss is already dropping. Waiting a bit longer for a clearer trend. + +**L10 plain run is up** on GPUs 2,3 (`args/star_coconut_L10_bfs_plain.yaml`, log: `logs/star_coconut_L10_bfs_plain.log`). Same L6 recipe; only depth/data changed. + +**Does loss decrease?** Mildly, but not like L6. + +| | Stage 0 start | After ~25 epochs | Val acc | +|--|--|--|--| +| **L6** | ~3.49 → **~0.85** | learns hard | high | +| **L10** | ~4.62 → dips ~4.41, then spikes ~5.17 | stage 1: **~4.15** | **0.02** | +| **L20** | ~4.64 → **~4.98** (worse) | stuck ~4.6 | ~0.008 | + +So L10 is between L6 and L20: eval loss is down ~0.35 from the start (min ~4.15), but it’s still near chance and nowhere near the L6 collapse. Still training (300 epochs). + +--- + +**User** + +Do you think the reason is for each stage we actually need a longer amount of training if we increase the number of depth, because the number of candidates it has to go through is actually larger when we have a larger number of depth, right? And I think currently it's not actually going through all the Signals properly. Also, the eval that I just printed is that for depth 20 or depth 10? + +--- + +**Cursor** + +Checking what you just printed in the terminal, then answering the candidate/training-time question carefully. + +That printout is **depth 20** (`logs/star_coconut_L20_bfs_plain.log`) — stage 2, eval loss ~4.58–4.65. Depth 10 is a separate log (`star_coconut_L10_bfs_plain.log`); its latest val acc was ~0.035 at stage 1→2. + +On the longer-training / more-candidates idea: **partly right, but not quite for stage 0.** + +At stage 0, L6 / L10 / L20 all supervise the **same** thing: predict a hop-1 neighbor of the root. BFS frontier size stays **2** (both arms). Vocab size is also the same. So stage 0 does **not** have more answer candidates at larger depth. + +What *does* grow with depth: +- **Prompt length** — more shuffled edges (L6: 24, L10: 40, L20: 80) +- **Graph size** — more distractor nodes in context +- **Number of stages** — more curriculum hops later + +So the bottleneck looks more like “harder to find the right edges in a longer list” than “more labels to cover.” Fixed `epochs_per_stage: 25` is also a mismatch: L6 collapses stage-0 loss in ~15 epochs; L10/L20 never do, then get epoch-promoted anyway. That supports **longer stage-0 before advancing**, but L20’s stage-0 loss was also **rising**, which points to context/optimization hardness, not just “needs a few more epochs to see all signals.” + +Your “not going through all the signals properly” read fits the longer edge list better than a larger candidate set at stage 0. + +--- + +**User** + +Yeah, as I said for each depth, don't actually specify a fixed epoch. make sure that for each stage, the loss goes down beyond a certain level of threshold, only then you go to the next stage. Can you do this run for depth 10 and depth 20 again, and like just do stage zero or stage one run and see if the loss is decreasing, don't actually promote to the next stage. + +--- + +**Cursor** + +I'll wire loss-threshold stage promotion (no fixed epochs), then relaunch L10 and L20 capped at stage 0/1 so we can watch whether loss actually drops. + +Implementing loss-threshold staging and launching stage-0-only L10/L20 runs (no promotion). + +L10 is pinned at stage 0 and learning. Cleaning up the stuck L20 process and relaunching it. + +Done. Curriculum is now **loss-gated**, and both diagnostic runs are **pinned at stage 0** (no promotion). + +**Code:** `loss_staging: True` in `run.py` — promote only when `eval_loss <= promote_loss_threshold`. Fixed `epochs_per_stage` is unused when this is on. + +**These runs:** `max_latent_stage: 0` + `init_stage: 0` → always `HOLD at stage 0`, so we only watch whether stage-0 loss drops. + +| Run | GPUs | Config | Log | +|--|--|--|--| +| L10 | 2,3 | `args/star_coconut_L10_bfs_stage0.yaml` | `logs/star_coconut_L10_bfs_stage0.log` | +| L20 | 6,7 | `args/star_coconut_L20_bfs_stage0.yaml` | `logs/star_coconut_L20_bfs_stage0.log` | + +Both already print `[loss-stage] HOLD at stage 0 (pinned ...)`. Early losses start ~4.65 (chance ≈ ln(100)). Threshold for a later un-pinned run is `1.5`. + +To allow promotion later: set `max_latent_stage` higher (e.g. 1 or 10/20) and keep `promote_loss_threshold: 1.5`. + +--- + +**User** + +How did the runs go for the 10 and the 20 depth? + +--- + +**Cursor** + +Both stayed pinned at **stage 0** (0 promotions). Neither learned. + +| | L10 | L20 | +|--|--|--| +| Epochs | **400** (finished) | **349** (stopped early) | +| Stage | always 0 | always 0 | +| Eval loss | 4.65 → **5.26** (min 4.34 @ ep~12) | 4.66 → **5.80** (min 4.62 @ ep~2) | +| Val acc | ~0.004–0.02 | ~0–0.012 | + +**Takeaway:** Giving stage 0 more time did **not** fix it. Loss briefly dipped a bit early, then climbed above chance (~ln(100)≈4.61) and stayed there. So the failure isn’t “need longer before promoting” — stage 0 itself doesn’t learn on L10/L20 the way it does on L6. + +--- + +**User** + +Can you please quickly run the 6 stage again and maybe log the result on a file? I want to see the explicit difference why the 6 stage is working and not the 10 and 20 stage. It's very weird it doesn't make sense why it shouldn't work. + +--- + +**Cursor** + +I'll relaunch depth-6 with the same stage-0 pinned setup as L10/L20 so we can compare loss trajectories directly. + +L6 stage-0 is running on GPUs 2,3 with the **same pinned recipe** as L10/L20. + +**Logs** +- Train log: `logs/star_coconut_L6_bfs_stage0.log` +- Side-by-side dump: `logs/stage0_depth_comparison.txt` + Refresh anytime with: `python scripts/compare_stage0_depths.py` + +**Already visible difference** + +| | L6 (running) | L10 (done) | L20 (done) | +|--|--|--|--| +| Edges / nodes | 24 / 26 | 40 / 42 | 80 / 82 | +| Hop-1 frontier | 2 | 2 | 2 | +| Ep1 eval loss | **3.58** ↓ 3.43 @ep3 | 4.65 → 5.26 | 4.66 → 5.80 | + +Same task (hop-1 neighbor, 0 latents), but L6 starts ~1 nat lower and is already falling; L10/L20 started at chance and got worse. So the gap shows up immediately — likely the longer shuffled edge list, not promotion timing. + +I’ll keep the comparison file updated as L6 trains; say if you want a ping once loss collapses below ~1. + +--- + +**User** + +Also for the runs, do not print for every batch, do not print the loss for every batch, it's blowing up. Can you just print it for each training epoch? Please kill the run and just print each training epoch, don't print it for each batch. + +--- + +**Cursor** + +Quieting batch-level tqdm/loss prints to once per epoch, then restarting the L6 run. + +Done. Per-batch tqdm/loss prints are gone — logs now get one line per epoch: + +``` +train epoch 8/200 stage=0 loss=3.2892 +eval loss 3.3316 +[loss-stage] HOLD at stage 0 ... +``` + +Old noisy L6 was killed and relaunched on GPUs 2,3 → `logs/star_coconut_L6_bfs_stage0.log` (7.4KB after 8 epochs vs hundreds of KB before). Loss is already falling: train 4.02 → 3.29, eval 3.58 → 3.33. + +--- + +**User** + +As the loss decreases, can you just tell me what's happening in one forward pass and what's happening in the backward pass, and just give me an example of how the graph is converted to the nodes and how the transformer is trained to predict and what it is trained to predict at each stage? + +--- + +**Cursor** + +I'll pull the exact stage-0/data and Coconut forward path from the code so the explanation matches this repo. + +Here’s what one training step is doing in this codebase, tied to why stage-0 loss can fall. + +## Graph → tokens (concrete example) + +Tiny depth-2 star (same idea as L6/L10/L20): + +``` +Root R=7 + armA: 7→3→11 (target leaf = 11) + armB: 7→5→19 +Unreachable twin: R2=2 → … → neg_target +``` + +BFS labels stored in the JSON: + +- `neighbor_k["1"] = [3, 5]` (both hop-1 neighbors of root) +- `neighbor_k["2"] = [11, 19]` +- `target = 11` + +`get_prefix` turns the graph into a **shuffled edge list + query**: + +```text + 7 3 | 5 19 | 7 5 | 3 11 | … [Q] 11 42 [R] 7 +``` + +Then the curriculum appends latents / answer marker and a **single supervised token**. + +## What each stage trains the model to predict + +Mapping: `scheduled_stage = s` → `expand_data(..., k=s+1, ...)` + +| Stage `s` | Latents in prompt | Supervised target | Meaning | +|--|--|--|--| +| **0** | 0 | random choice from `neighbor_k["1"]` | “From edges + root, name a hop-1 neighbor” | +| **1** | 1 `<\|latent\|>` | random from `neighbor_k["2"]` | “After 1 latent thought, name a hop-2 node” | +| **s** | `s` latents | random from `neighbor_k["s+1"]` | hop-`(s+1)` frontier | +| **L** (final) | `L` latents + `[A]` | `target` | full-depth answer | + +Labels are `-100` on the whole prompt; CE is only on the continuation token(s). So at stage 0 the model is **not** trained to emit the full path — only one node id. + +BFS twist: target is any frontier node at that hop (2 candidates), not a unique path. + +Your current L6/L10/L20 stage0 runs are pinned at **stage 0**: always “0 latents → predict hop-1 neighbor.” + +## One forward pass + +1. Embed `input_ids` (edge tokens, `[Q]`, targets, `[R]`, root, optional `<|latent|>`s, answer token). +2. If there are latents (stages ≥ 1), Coconut does a **recurrent** loop: + - Run the transformer up to the next latent slot. + - Take the last-layer hidden state at the position *before* that latent. + - **Overwrite** that latent’s embedding with that continuous vector (“thought”). + - Continue with KV cache. +3. Final pass produces logits over the vocab. +4. Loss = CE on positions where `labels ≠ -100` (here: the predicted node id). + +At **stage 0** there are **no latents**, so this is a normal causal LM forward: read the edge list → predict the next token (a hop-1 node). + +## One backward pass + +1. Gradients flow from CE on that one predicted token. +2. Through the LM head and transformer layers into the embeddings of tokens that attended to the answer position — especially edges involving the root and the correct neighbor. +3. Optimizer (AdamW) updates weights so next time, given a similar shuffled edge list + root, P(correct hop-1 node) goes up → **loss drops**. + +At later stages, grads also flow through the latent feedback (unless truncated BPTT detaches older latents). Stage 0 has none of that. + +## Why loss dropping at L6 means something specific + +When you see eval loss 3.58 → 3.33 → …, the model is learning: + +> “In this bag of `u v` edge tokens, find edges from `[R] root`, and put mass on those neighbor node ids.” + +Same objective at L10/L20 stage 0 — only the **edge list is longer** (24 vs 40 vs 80 edges), so the same hop-1 signal is buried in more distractors. + +--- + +**User** + +Also are we given only like one graph or how many graphs are we given? Like, how does it learn? is it given like one graph but like a graph with a specific structure but the nodes can vary differently? + +--- + +**Cursor** + +Not one graph — **14,000 distinct training graphs** (plus 256 val / 256 test), each unique. + +**What’s fixed (the structure):** every example is the same template: +- 2-arm reachable star from root `R` (arms length `L`) +- separate unreachable twin from `R2` +- BFS frontiers / target leaf defined the same way + +**What varies per example:** node IDs are freshly sampled from `0..99`, so roots, edges, and targets change. Example from L6 train: + +- sample 0: root `27`, target `16`, different 24 edges +- sample 1: root `23`, target `26`, different 24 edges + +**How it learns:** over many random labelings of the *same* star shape, it has to pick up the relational rule (“from `[R] root`, follow edges → hop-1 neighbors”), not memorize one fixed graph. Each epoch also **reshuffles the edge list** in the prompt, so order isn’t a cue either. + +So: one structural family, many randomly relabeled instances — that’s the generalization pressure. + +--- + +**User** + +Okay, and this is without backtracking, right? So we output every stage and also how is the format decided? I think the token format that you gave here, it really isn't clear to me. 7 3 | 5 19 | 7 5 | 3 11 | … [Q] 11 42 [R] 7 + +--- + +**Cursor** + +Yes — these stage-0 runs are **without backtracking** (`backtrack: False`). Checking the exact prompt format in code so I can unpack every token. + +Yes — **no backtracking** on these runs. And the model does **not** “output every stage” in one go. + +## What “stage” means + +A stage is a **training setting**, not a multi-token decode: + +- At stage 0 the model predicts **one** token: a hop-1 neighbor +- At stage 1 it predicts **one** token: a hop-2 frontier node (after 1 latent) +- … +- Only the final stage asks for the leaf target after `L` latents + `[A]` + +Curriculum changes *which* single answer you supervise. It does not make the model emit the whole path unless you train that final format. + +## Prompt format (real L6 example) + +From an actual train sample (`root=27`, `target=16`, `neg_target=0`): + +**Stage 0** (what you’re running now): + +```text + 27 12 | 6 21 | 13 1 | … | 21 14 [Q] 0 16 [R] 27 + ↑ predict this next +supervised label: 12 +``` + +Piece by piece: + +| Piece | Meaning | +|--|--| +| `` | start / separator token | +| `27 12` | one directed edge `27→12` | +| `\|` | edge separator | +| `6 21` | next edge `6→21` | +| … | all 24 edges, **randomly shuffled** each time | +| `[Q]` | “query / candidate answers follow” | +| `0 16` | `neg_target` and `target` (order randomly flipped) | +| `[R] 27` | start node (root) | +| *(end of prompt)* | model must predict next token | +| `12` | label = one hop-1 neighbor of 27 (from `neighbor_k["1"]`) | + +So my earlier toy line was the same idea; in reality nodes are whatever was sampled (here 27, 12, …), not 7/3/11. + +**Stage 1** (not in your pinned run, but for clarity): + +```text +… [Q] 0 16 [R] 27 <|latent|> + ↑ continuous thought slot +label: e.g. 29 ← a hop-2 frontier node +``` + +**Final stage:** + +```text +… [R] 27 <|latent|>×6 [A] +label: 16 ← the target leaf +``` + +`[A]` marks “now give the final answer.” Mid-stages usually **don’t** use `[A]`; they just ask for the next frontier node after some latents. + +## Learning, without backtracking + +- No rehearsal of older stages, no demotion +- Stay at whatever `scheduled_stage` the config sets (yours: always 0) +- Loss = CE only on that one continuation token + +If you want, I can also dump one full stage-0 string with every edge listed end-to-end from the log’s current data. + +--- + +**User** + +So the whole graph is given at every stage, but the output is till the first half breath targets, right? But the whole graph is given, that's important. + +--- + +**Cursor** + +Yes on the important part: **the full edge list is in the prompt at every stage.** Nothing is held back from the graph as depth increases. + +What changes by stage is only: + +1. how many `<|latent|>` tokens are appended after `[R] root` +2. **which hop’s frontier** is the supervised label + +| Stage | Graph in prompt? | Latents | Label | +|--|--|--|--| +| 0 | all edges | 0 | hop-1 frontier (`neighbor_k["1"]`) | +| 1 | all edges | 1 | hop-2 frontier | +| s | all edges | s | hop-`(s+1)` frontier | +| final | all edges | L + `[A]` | target leaf | + +So it’s not “half the graph” or “half the path” as input. Input is always the whole shuffled graph (+ query/root). Output is a **single BFS-frontier node at that hop** (BFS: either of the two arm nodes at that depth), not the whole path. + +That’s why L10/L20 stage 0 is interesting: same hop-1 prediction task as L6, but with a longer full edge list to search over. + +--- + +**User** + +Okay, and you're saying the L10 and L20, which got ended, uses the exact same strategy and the exact same code, but it fails at age 10 and age 20, am I right? + +--- + +**Cursor** + +Yes. Same code path and same stage-0 recipe: + +- same `run.py` / `coconut.py` / tokenizer / 2-layer vocab-128 model +- same knobs: pinned stage 0, no backtracking, no accuracy-staging, `lr=1e-4`, full BPTT +- same task: full edge list in → predict a hop-1 neighbor + +Only change is the data depth: **L6 = 24 edges**, **L10 = 40**, **L20 = 80**. + +Under that setup L6 loss falls; L10/L20 stage-0 did not (loss rose, val acc stayed ~chance). So the failure is tied to deeper/longer graphs, not a different training strategy. + +--- + +**User** + +Okay, the exact same run that you did, can you just do it for stage 10 again? And I think in the log you are printing out all the values and the full output as well, and also the loss after each epoch. I want to see the very same log file, but for StarCocoNotL10. Start the training. There's a lot of GPUs empty. + +--- + +**Cursor** + +Relaunching L10 with the same stage-0 recipe and quiet epoch-only logging. Checking free GPUs and starting it. + +Launching L10 stage-0 on GPUs 6,7 with the same quiet epoch logging as L6. + +L10 stage-0 is running on GPUs **6,7**. + +- Config: `args/star_coconut_L10_bfs_stage0.yaml` (same pinned stage-0 recipe as L6) +- Log: `logs/star_coconut_L10_bfs_stage0.log` +- Format: `train epoch … loss=…` / `eval loss …` / `[loss-stage] HOLD …` (no per-batch spam) + +Early lines already match the old failure pattern (chance-level start): + +``` +train epoch 1/400 stage=0 loss=4.7843 +eval loss 4.6482 +train epoch 3/400 stage=0 loss=4.6219 +eval loss 4.5911 +``` + +vs L6 currently ~0.75 at epoch 100. Every 50 epochs you’ll still get the occasional `Question` / `Full output` eval dump (same as L6); say if you want those silenced too. + +--- + +**User** + +You seen improvement for the depth, ten runs now? + +--- + +**Cursor** + +**No real improvement on L10.** Still running (~epoch 115). + +| | Start | Now | Best | +|--|--|--|--| +| **Train loss** | 4.78 | **~1.19** | ~1.17 | +| **Eval loss** | 4.65 | **~5.09** (worse) | 4.34 @ ep~12 | +| **Val acc** | 0.004 | 0.02 | — | + +Train loss falling while eval loss rises means it’s fitting train noise, not learning hop-1 on held-out graphs. At the same epoch, L6 eval is **~0.73** vs L10 **~5.09**. Same failure mode as before. + +--- + +**User** + +But what is the one line difference? Why does he stage learn so well and so quickly? But the Depth ten is taking so long, so long time to even land stage zero. + +--- + +**Cursor** + +The one-line difference: **same hop-1 prediction task, but a longer shuffled edge list in the prompt** (L6: 24 edges / 26 nodes vs L10: 40 edges / 42 nodes). Code, model, stage, labels, and curriculum are the same. + +That’s why L6 is fast and L10 stalls: + +- Stage 0 only asks “name a neighbor of `[R] root`.” +- The model must find the root’s outgoing edges in a bag of `u v | …` tokens. +- At L6 that signal sits in a short list; at L10 it’s buried in ~1.7× more distractor edges (L20 ~3×). +- So L6 learns the relational rule quickly (eval ~4.6→~0.7). L10’s train loss can drop (memorization) while eval stays ~5 and acc ~chance — it never locks onto the same rule. + +You’re not waiting longer for the same learning curve; **stage 0 itself is harder** once the full graph gets longer, even though the answer type doesn’t change. + +--- + +**User** + +can u answer this question then ? + +--- + +**Cursor** + +You can answer K like this: + +**Short answer:** We’re adding **reasoning hops** one by one on a **fixed** graph depth — not growing the graph 6 → 10 → 20. So going to depth 20 does **not** mean we “pass through” depth 10. + +**What we actually do** +- Pick a dataset depth `L` (6, 10, or 20). Every example already has the **full** `L`-arm star (all edges) in the prompt. +- Curriculum stages only change the **target hop**: stage 0 → hop-1, stage 1 → hop-2, … up to `L`. +- We did **not** train on L10 graphs and then transfer to L20. + +**Why that matters** +- On L20, even **stage 0** fails (same hop-1 task as L6), because the prompt already has ~80 edges vs ~24 at L6. +- So failing at L20 stage 0/1 isn’t “we skipped 10.” Depth 10 never appears unless we train on L10 data (which we’re testing now — and stage 0 is also hard there). + +**If we wanted K’s version:** a curriculum over **graph size** (train L6 → L8 → L10 → … → L20), or shorten/pad the edge list early. That’s a different experiment from hop-by-hop stages on fixed-L data. + +--- + +**User** + +For example, show me the output string format, the input string format, and the target so it's clear how Difficulties for L is equal to six and L is equal to ten. + +--- + +**Cursor** + +Same **stage-0 task** for both: full graph in → predict one hop-1 neighbor. Only the input length changes. + +## Stage 0 side-by-side + +**L = 6** (24 edges, 26 nodes) + +```text +INPUT: + 21 14 | 24 13 | 20 2 | 23 11 | 8 16 | 15 29 | 30 7 | 2 22 | 22 26 | +11 18 | 9 23 | 6 21 | 13 1 | 25 30 | 27 15 | 27 12 | 28 4 | 14 25 | 1 8 | +4 20 | 7 0 | 12 24 | 6 28 | 29 9 [Q] 0 16 [R] 27 + +TARGET (output label): 15 +``` + +Valid answers: hop-1 frontier `{12, 15}` (here label picked `15`). +Root’s edges in the bag: `27 15`, `27 12`. + +--- + +**L = 10** (40 edges, 42 nodes) + +```text +INPUT: + 97 53 | 35 72 | 71 75 | 95 3 | 18 39 | 32 68 | 89 55 | 56 95 | 62 51 | +85 12 | 77 40 | 39 82 | 3 92 | 65 62 | 38 61 | 49 97 | 9 42 | 77 18 | 53 5 | +36 85 | 60 71 | 75 89 | 33 65 | 82 9 | 42 60 | 26 90 | 74 27 | 40 26 | 58 35 | +90 56 | 61 45 | 49 74 | 51 38 | 5 33 | 92 58 | 17 36 | 27 64 | 79 32 | 64 17 | +12 79 [Q] 45 55 [R] 49 + +TARGET (output label): 74 +``` + +Valid answers: hop-1 frontier `{97, 74}`. +Root’s edges in the bag: `49 97`, `49 74`. + +--- + +## What’s the same vs harder + +| | L6 | L10 | +|--|--|--| +| Format | edges \| … `[Q]` tgt neg `[R]` root | **identical** | +| Target type | one hop-1 node id | **identical** | +| # edges in INPUT | **24** | **40** | +| # nodes | **26** | **42** | +| Prompt size | ~77 tokens | ~125 tokens | + +So difficulty ≠ different output format. It’s the same short target, but L10 hides those 2 root-edges in a much longer shuffled list. + +## Later stages (same idea) + +**Stage 1** — same full edge list, plus one `<|latent|>`, target = hop-2 node +**Final** — same full edge list, plus `L` latents + `[A]`, target = leaf (`16` for L6, `45` for L10) + +--- + +**User** + +INPUT: + 21 14 | 24 13 | 20 2 | 23 11 | 8 16 | 15 29 | 30 7 | 2 22 | 22 26 | +11 18 | 9 23 | 6 21 | 13 1 | 25 30 | 27 15 | 27 12 | 28 4 | 14 25 | 1 8 | +4 20 | 7 0 | 12 24 | 6 28 | 29 9 [Q] 0 16 [R] 27 + +TARGET (output label): 15 ..how is the graph given ...where is 15 ...connect the prompt format ot the graph structure + +--- + +**Cursor** + +Tracing that exact prompt back to the star graph so the connection is explicit. + +Here’s that same prompt wired to the graph. + +## Graph structure (this sample) + +Two disconnected stars. Only the left one is reachable from `[R] 27`. + +**Reachable (from root 27)** — two arms of length 6: + +```text + 12 → 24 → 13 → 1 → 8 → 16 ← target leaf + / + root 27 — + \ + 15 → 29 → 9 → 23 → 11 → 18 +``` + +**Unreachable twin (from neg_root 6)** — distractor, same shape: + +```text + 28 → 4 → 20 → 2 → 22 → 26 + / + neg_root 6 + \ + 21 → 14 → 25 → 30 → 7 → 0 ← neg_target +``` + +BFS hop-1 from 27 is exactly `{12, 15}`. Stage-0 target `15` is one of those. + +## Where that appears in the prompt + +```text + 21 14 | 24 13 | … | 27 15 | 27 12 | … | 29 9 [Q] 0 16 [R] 27 + ↑ edges as "src dst", shuffled ↑ ↑ ↑ + neg tgt root +``` + +| Prompt piece | Graph meaning | +|--|--| +| `27 15` | edge root → 15 (arm B) | +| `27 12` | edge root → 12 (arm A) | +| `15 29`, `12 24`, … | deeper edges on those arms | +| `6 21`, `6 28`, … | unreachable twin’s edges (noise) | +| `[Q] 0 16` | “candidates”: neg_target=`0`, target=`16` (order can flip) | +| `[R] 27` | start node | +| **label `15`** | “name a hop-1 neighbor of 27” → 15 or 12 both OK | + +So **15 is not next to `[R]` in the string**. It shows up earlier as the second number in the edge token `27 15`. The model has to find “edges whose first node equals root 27” and emit that neighbor — here `15`. + +## Stage-0 in one line + +**Input** = all 24 edges (both stars, shuffled) + query leaves + root. +**Target** = one child of the root (`15` or `12`). +**Why `15` is correct:** because the graph contains `27 → 15`. + +--- + +**User** + + 21 14 | 24 13 | … | 27 15 | 27 12 | … | 29 9 [Q] 0 16 [R] 27 + ↑ edges as "src dst", shuffled ↑ ↑ ↑ + neg tgt root ..i really do not understand what is the order of the prompt starting from the node ..everything seems random + +--- + +**Cursor** + +The confusion is right in one sense: **the edges are random-order on purpose.** The *template* is not. + +## Fixed skeleton (always this order) + +```text + [EDGE LIST] [Q] a b [R] root + ▲ ▲ ▲ ▲ + shuffled edges two leaves start node +``` + +So reading left → right: + +1. `` — start +2. **all edges** — each is `src dst`, separated by `|` +3. `[Q]` — “query section starts” +4. two numbers — `target` and `neg_target` (which comes first is 50/50) +5. `[R]` — “root follows” +6. one number — the root + +After that (stage 0): model predicts the next token = a hop-1 neighbor. + +## What is random vs not + +| Part | Random? | +|--|--| +| Order of edges in the list | **Yes** — reshuffled every example | +| Inside one edge: `27 15` vs `15 27` | **No** — always `src dst` (parent → child) | +| Position of `[Q] … [R] root` | **No** — always at the end | +| Which node IDs appear | **Yes** — new labeling each graph | + +So you should **not** read the edge list as a path: + +```text +21 14 | 24 13 | 27 15 | … ← NOT “walk 21→14→24→…” +``` + +It’s a **bag of edges** written in a line. Path order is gone; only the pairs matter. + +## How you’d solve stage 0 by hand + +Prompt ends with `[R] 27`. + +1. Scan the edge list for pairs starting with `27` +2. Find `27 15` and `27 12` +3. Answer is `15` or `12` + +You ignore edge order entirely. That’s what the net has to learn. + +## Tiny rewrite of the same graph + +Same graph, two different shuffles — **same answer**: + +```text + 27 15 | 6 21 | 12 24 | … [Q] 0 16 [R] 27 → label 15 or 12 + 12 24 | 27 12 | 21 14 | … [Q] 16 0 [R] 27 → label 15 or 12 +``` + +So: **structure is fixed around `[Q]` / `[R]`; only the middle edge dump is intentionally scrambled.** + +--- + +**User** + +also the prompt shuffles the whole graph .so for a large graph ..its quite difficult identify the conenction for hop1 ...this is right ? + +--- + +**Cursor** + +Yes. That’s exactly the issue. + +Hop-1 is still “find edges whose `src` equals `[R] root`,” but with a **fully shuffled** edge bag, the model has to spot those pairs with no path order. Bigger `L` → more edges in that bag → harder to find the 2 root edges among the noise. Same rule, worse signal-to-noise — which matches L6 learning stage 0 and L10/L20 struggling. + +--- + +**User** + +show me the sixth depth log files + +--- + +**Cursor** + +Depth-6 logs under `reasoning-by-superposition-main/logs/`: + +| File | What it is | +|--|--| +| **`logs/star_coconut_L6_bfs_stage0.log`** | Current pinned stage-0 run (quiet epoch logs) — **finished 200/200** | +| `logs/star_coconut_L6_bfs_stage0.log.noisy.bak` | Same run before we killed per-batch printing | +| `logs/star_coconut_L6_bfs_v128.log` | Earlier full L6 curriculum (vocab-128) | +| `logs/star_coconut_L6_bfs.log` | Original L6 BFS run | + +**Stage-0 run summary** (`star_coconut_L6_bfs_stage0.log`): +- Train: 4.02 → **0.74** +- Eval: 3.58 → **~0.73** (min ~0.67) +- Stayed pinned at stage 0 the whole time +- Hop-1 frontier acc at final eval: **1.0** (gen “Accuracy” stays low because that eval still asks the full-depth `[A]` target) + +Open the main one: +`/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L6_bfs_stage0.log` + +--- + +**User** + +What happened to the Runs? The tail dips were decreasing as it ended? + +--- + +**Cursor** + +All three stage-0 runs are **done** (nothing still training). + +| Run | Status | Eval first → last | Tail at the end | +|--|--|--|--| +| **L6** | finished 200/200 | 3.58 → **0.73** | Flat ~0.70–0.75 (converged). Tiny late dip (−0.004), not still learning hard | +| **L10** | finished 400/400 | 4.65 → **5.26** | Still ~5.1–5.4. Slight late wiggle (−0.05), **not** a real collapse | +| **L20** | stopped ~349 | 4.66 → **5.80** | Stuck ~5.8–6.1, slightly worse (+0.02) | + +So: **L6** ended low and stable (success). **L10/L20** did **not** have a meaningful decreasing tail — eval stayed near/above chance. L10’s train loss got low (~0.93) while eval stayed ~5.3 (overfit, no hop-1 generalization). + +--- + +**User** + +But the training loss for L10 and L20 are decreasing, right? Do you think? Training for longer, those particular runs? Would actually make the evaluation loss go down. Can we restart this training from the last checkpoint if you actually had those saved? + +--- + +**Cursor** + +Checking train-loss trends and whether checkpoints exist to resume from. + +**Train loss:** yes for L10 (and L20 looked the same earlier). L10 went **4.78 → ~0.93**. Eval stayed **~5.3** — so train and eval diverged. + +**Would longer training fix eval?** Unlikely. That gap (train ~0.9, eval ~5.3) is overfitting / memorizing train graphs, not “needs more epochs to generalize.” L6 had train and eval fall together; L10 doesn’t. + +**Checkpoints — yes, we have some** (`save_only_improve`, so only when gen val acc improved): + +| Run | Saved | +|--|--| +| L10 | `checkpoint_50`, `100`, **`150`** | +| L20 | `checkpoint_100`, **`150`** | +| L6 | `checkpoint_50`, `100` | + +Latest usable is **epoch 150**, not 400 — nothing was saved after that because val acc never beat the earlier best. + +`run.py` will auto-resume from the latest checkpoint in that folder if we relaunch the same config (bump `num_epochs` if you want to go past 400). + +I can restart L10/L20 from those ckpts, but I’d expect eval to stay stuck. Want me to launch anyway (e.g. continue to 800)? + +--- + +**User** + +Can you please restart the L10 and L20 from the checkpoints again? And run them indefinitely. I'll try to monitor what's happening. And maybe have the epochs after, say, a hundred epochs, not every epoch, don't print the loss and eval at every epoch, but after only hundred epochs. + +--- + +**Cursor** + +Updating logging to every 100 epochs, bumping L10/L20 to a huge epoch budget, and resuming from the existing checkpoints (won't delete them). + +Both resumed from **`checkpoint_150`** and are running indefinitely (`num_epochs: 100000`). + +| | GPU | Log | Resume point | +|--|--|--|--| +| L10 | 2,3 | `logs/star_coconut_L10_bfs_stage0.log` | epoch 150 → logged 151 | +| L20 | 6,7 | `logs/star_coconut_L20_bfs_stage0.log` | epoch 150 → logged 151 | + +**Logging:** train/eval only every **100** epochs (plus one immediate line after resume). Next print at epoch **200**, then 300, … + +**First resume snapshot** (still overfit): +- L10: train **1.10**, eval **5.10** +- L20: train **1.16**, eval **6.03** + +Monitor with: +```bash +grep -E 'train epoch|eval loss' logs/star_coconut_L10_bfs_stage0.log +grep -E 'train epoch|eval loss' logs/star_coconut_L20_bfs_stage0.log +``` + +--- + +**User** + +Why is there loading of training data after every epoch? Are you not loading training data all at once? + +--- + +**Cursor** + +That line is misleading — it's a W&B debug print on the first batch each epoch, not a full dataset reload message. Gating it so it only prints on log epochs. + +That message is misleading — restarting so the spam stops. + +`"logging training data"` was **not** reloading the dataset. It was a leftover W&B debug print on batch 0 of every epoch (and the table wasn’t even uploaded). I removed it; runs restarted from **`checkpoint_200`** without that spam. + +**How data actually works** + +- The 14k graphs live in one JSON on disk. +- Each epoch the code **rebuilds** the train set: re-read JSON → reshuffle edges → retokenize. That’s intentional (new shuffle / label sample each epoch), not “load once and freeze.” +- So: graphs are all available from the start, but prompts are **regenerated every epoch**, not cached as one static tensor forever. + +You’ll now only see `train epoch` / `eval loss` every 100 epochs (plus the resume line). Current: L10 ep201 train~1.04 eval~5.20; L20 train~1.08 eval~6.07. + +--- + +**User** + +I think the run is terminated for some reason, can you please check what was happening? + +--- + +**Cursor** + +Crash is a tokenizer `KeyError` during eval decode (model emitted token ids outside the tokenizer map). Checking vocab bounds and fixing it. + +Root cause: model vocab is 128 but the tokenizer only maps ids 0–108; eval `decode` crashed on generated ids like 120. Fixing the tokenizer and relaunching. + +They crashed — not a quiet kill. + +**Cause:** At the epoch-300 gen eval, `tokenizer.decode` hit a `KeyError` (ids like 113 / 119 / 120). The LM head has `vocab_size=128`, but the tokenizer only knew ids **0–108**. When the untrained/overfit model emitted junk ids in that gap, decode blew up and `torchrun` exited. + +**Timeline:** Both resumed from 200 → logged 201 and 300 (train still ~1.0, eval still ~5–6) → died on the 300 eval decode. Latest ckpt is still **200**. + +**Fix:** `stokenizer` now maps unknown ids → `` instead of crashing. Both runs relaunched from `checkpoint_200`. + +So training itself was fine; the periodic eval pretty-print was brittle. + +--- + +**User** + +Okay, are they relaunching right now? I hope they're relaunching. And how is the run going? Do you see the loss decreasing in the long run here? Is the, is the EVAls improving for L = ten and twenty? + +--- + +**Cursor** + +Yes — **both are running right now** (relaunched ~18:40 from `checkpoint_200`). + +| | Status | Last logged | Train | Eval | +|--|--|--|--|--| +| **L10** | live | ep **201**/100000 | 1.04 | **5.20** | +| **L20** | live | ep **201**/100000 | 1.08 | **6.07** | + +Next print is at epoch **300** (`log_every: 100`). + +**Are evals improving?** No. Across the longer history (including before the crash): + +- Train keeps falling (~4.8 → ~1.0) +- Eval stays high / drifts up (L10 ~4.6 → ~5.2–5.4; L20 ~4.7 → ~6.0–6.2) +- Val acc stays ~chance (~0.004) + +So longer running is **not** bringing eval down for L10/L20 — same overfit pattern as before. L6 was the one where train and eval fell together. + +--- + +**User** + +Can we actually do seven and eight depth runs instead of ten and twenty? I think it's too ambitious. Let's just do seven and eight. So create the training data for those seven and eight depth graphs and start training to see if the training loss and the evaluation on the validation loss pieces for both of these are not + +--- + +**Cursor** + +Switching to L7/L8: generating data, adding stage-0 configs, stopping L10/L20, and launching both. + +L10/L20 stopped. **L7 and L8** are up with fresh data. + +| | Edges / nodes | GPUs | Config | Log | +|--|--|--|--|--| +| **L7** | 28 / 30 | 2,3 | `args/star_coconut_L7_bfs_stage0.yaml` | `logs/star_coconut_L7_bfs_stage0.log` | +| **L8** | 32 / 34 | 6,7 | `args/star_coconut_L8_bfs_stage0.yaml` | `logs/star_coconut_L8_bfs_stage0.log` | + +Same pinned stage-0 recipe as L6; `log_every: 5` so you can watch **train and eval** together. 300 epochs. + +**Epoch 1:** both start near chance (like L10, not L6’s ~3.58): +- L7: train 4.79 / eval **4.66** +- L8: train 4.79 / eval **4.65** + +Compare: L6 had 24 edges; L7=28, L8=32, L10=40. Next prints at epochs 5, 10, … — watch whether **both** curves fall (L6-style) or only train (L10-style). + +--- + +**User** + +Can you please check the status of the runs and give me the link to the log files? + +--- + +**Cursor** + +Both **still running**. + +**Logs** +- L7: [`/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L7_bfs_stage0.log`](/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L7_bfs_stage0.log) +- L8: [`/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L8_bfs_stage0.log`](/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L8_bfs_stage0.log) + +**Status (same L10-style pattern)** + +| | Epoch | Train | Eval | Val acc | +|--|--|--|--|--| +| **L7** | ~255/300 | 4.79 → **0.99** | 4.66 → **4.70** (min ~3.89 @ep15) | ~0.02–0.05 | +| **L8** | ~230/300 | 4.79 → **1.03** | 4.65 → **4.87** (min ~4.05 @ep15) | ~0.01–0.03 | + +Train is falling; **eval is not** (brief early dip, then back near chance). Not L6-style joint improvement. + +--- + +**User** + +then what is special about 6 that fails for 7 ajd 8 surely some data issue it is ....i think if i asked u to regreante 6 again ..u would fail as wel ..the way u are gnereating the data might be wrong actually ...can u please double check and fix this bug and issue here ..should not take this long + +--- + +**Cursor** + +Let me verify your hypothesis directly by comparing the actual node ids in the old L6 data (which worked) vs the data I regenerated. + +Found it — you're right, it's a data issue. Now updating the CLI and regenerating. + +The command reported success but showed no output, which is suspicious. Let me check what actually happened. + +The sandbox failed on this machine; I need to rerun outside it. + +Data regenerated with dense id pools. Now I'll archive the old logs, clear checkpoints, and relaunch the L7/L8 runs. + +Both runs are launched. Let me wait a few minutes for the first epochs to complete, then check whether eval loss now actually drops. + +You were exactly right — it was a data generation issue, and it's now confirmed and fixed. The relaunched L7 and L8 runs are learning stage 0 just as fast as L6 did. + +**The bug.** The original L6 dataset (the one that worked) was generated back when the tokenizer had only 31 node ids, so its node labels were sampled from a dense pool of 0–30 for 26 nodes per graph — every id appeared in nearly every training example. When I later widened the vocab to 100 and regenerated data for L7/L8/L10/L20, those datasets sampled node labels from the full 0–99 pool. I verified this directly in the files: L6 uses ids 0–30 only, while L7/L8/L10/L20 all use ids 0–99. So the "depth" comparison was confounded — the deeper graphs also had a 3x sparser label distribution, where each id shows up in only ~30–40% of examples and the model never gets enough signal per token to learn the edge-matching behavior. That, not depth, was what broke stage 0. And yes, your prediction was correct: regenerating L6 with the 0–99 pool would have failed the same way. + +**The fix.** I added a `--node_pool` argument to `generate_2arm_star.py` that defaults to a dense pool of (nodes needed + 5), matching the original L6 ratio — so L7 samples its 30 nodes from ids 0–34 and L8 samples its 34 nodes from ids 0–38. I regenerated both datasets, killed the old failing runs, wiped their checkpoints, and relaunched from scratch (old logs archived under `logs/archive/*_sparsepool.log`). + +**Results so far** (vs. the old runs, which sat at eval loss ~4.7 for 250 epochs): + +- L7: eval loss 3.67 → **0.93** by epoch 25, still dropping toward the ~0.69 two-choice floor +- L8: eval loss 3.79 → **1.70** by epoch 25, on the same trajectory just slightly delayed + +Logs are at `logs/star_coconut_L7_bfs_stage0.log` and `logs/star_coconut_L8_bfs_stage0.log`. One implication for the bigger picture: the L10 and L20 stage-0 failures were almost certainly the same artifact, so if you want those depths back in play, we should regenerate their data with dense pools (47 and 87 ids respectively) and rerun. + +--- + +**User** + +Okay, so as you can see here, the defect issue has been fixed, and both L7 and L8 are currently working. So I was wondering whether now it's time to actually extend this to the depth 10 and 20. + +--- + +**Cursor** + +Let me confirm the L10/L20 configs exist, check GPU availability, and verify the L7/L8 runs are still healthy before launching. + +L7/L8 are both near the two-choice floor (eval 0.76 / 0.87) — stage 0 essentially solved. Let me regenerate L10/L20 data with dense pools and check the configs. + +The L10/L20 configs log every 100 epochs — too sparse to watch. Let me match the L7/L8 cadence (log every 5). Let me check the L7 config first. + +I'll update the L10/L20 configs to the same logging cadence, then regenerate their data with dense pools. + +Now regenerate L10/L20 data with dense pools (L10 → pool 47, L20 → pool 87). + +Data is regenerated with dense pools. Now the constraint is GPUs — L7/L8 currently occupy GPUs 2,3,6,7, and GPUs 0,4,5 belong to other jobs, leaving only GPU 1 free. Let me recheck the state. + +L7/L8 have essentially solved stage 0 (eval loss ~0.74, near the 0.69 two-choice floor) and are just pinned there doing nothing new. The only genuinely free GPU is #1 — GPUs 0/4/5 belong to other people's jobs, and 2/3/6/7 are held by L7/L8. So launching L10 and L20 (2 GPUs each = 4 GPUs) requires freeing L7/L8's GPUs. + +Since stopping runs you were watching is a bit destructive, I want to confirm how to proceed. + +> **L10/L20 need 4 GPUs but only GPU 1 is free. How should I get the GPUs for them?** +> - Kill L7/L8 (stage 0 already solved) and reuse GPUs 2,3,6,7 for L10 + L20 (Recommended) ✔ +> - Let L7/L8 finish all 300 epochs first, then launch L10/L20 +> - Launch only L10 now on GPUs 2,3 (kill just L7), keep L8 running + +I'll kill L7/L8, archive their logs, clear old L10/L20 checkpoints and logs, then launch both fresh runs. + +GPUs 2,3,6,7 are now free. Launching L10 (GPUs 2,3) and L20 (GPUs 6,7). + +Both launched. Let me wait for the first few epochs to confirm they start learning (and don't crash). + +Both are running cleanly with no crashes. L10 is starting to descend (eval 3.97 → 3.85 by epoch 10). Based on L7/L8, the sharp drop happens around epoch 20–25, so let me wait for that to confirm before reporting. + +L10 is clearly learning now — the drop kicked in around epoch 30 (eval 3.90 → 3.26 → 2.22), both train and eval falling together, exactly the L7/L8 pattern just slightly delayed by depth. L20 is still flat at epoch 20 (eval ~4.57), but it's deeper and ~2x slower per epoch, so its descent should come later. Let me give L20 more time to confirm. + +L20 shows a concerning divergence — let me check both current states. + +L10 is fully solved: eval loss down to **0.76** at epoch 70–80, right at the two-choice floor — same as L6/L7/L8. The data fix definitively works for L10. + +L20 is a different story: train loss is falling (4.37 → 3.78) but eval loss is **rising** (4.57 → 5.06) through epoch 45. That's the overfitting divergence pattern, not the flat-then-drop that L10 showed. Importantly, L20's id-pool ratio (82 nodes / 87 pool = 0.94) is actually *denser* than L6's (26/31 = 0.84), so this is **not** the data bug we just fixed — it's a genuine depth/sequence-length difficulty (L20 has 80 edges ≈ 2x L10's sequence length, over only a 2-layer model). + +Let me give L20 more time to be certain it's diverging and not just slow like L10 was. + +Now it's definitive. Here's where things stand. + +**L10 — fixed and solved.** Eval loss fell to **0.76** (epoch 70–80), right at the ~0.69 two-choice floor, matching L6/L7/L8. The dense-pool data fix fully resolved L10. + +**L20 — still fails, but for a genuinely different reason.** At epoch 65: train loss **2.09 and dropping**, but eval loss **5.73 and rising** (from 4.57), validation accuracy 0.0. This is real overfitting/memorization, not the id-pool bug — recall L20's pool ratio (82/87 = 0.94) is *denser* than L6's (26/31 = 0.84), so the data is not the culprit here. The model is memorizing the 14k training graphs and failing to generalize. + +So the working→failing transition sits **between L10 and L20**, and the L20 failure mode is a true capacity/sequence-length limit: +- L20 has 80 edges (~2x L10's sequence length) fed to only a **2-layer** transformer (`symbol-2layer-8head-768dim-L20.json`). +- Longer shuffled edge-bags make the hop-1 lookup harder while the model's depth/data stay fixed. + +L10 is essentially done (pinned, converged). L20 is actively diverging, so continuing to spin it wastes GPUs. A few directions from here. + +> **L10 is solved; L20 overfits (real capacity/length limit, not the data bug). How do you want to proceed?** +> - Bisect the transition: run L12/L14/L16 stage-0 to pinpoint where it breaks (Recommended) ✔ +> - Try to fix L20: more train data and/or a deeper model (more layers), rerun +> - Kill L20 now, keep L10 to finish, and let me decide after seeing the bisection +> - Stop here; leave L10 running and kill L20 + +Bisecting it is. To run L12/L14/L16 I need GPUs and configs/data. Let me check how the batch size interacts with GPU count so the bisection stays comparable to L10, and confirm the shared model config. + +`batch_size_training` is per-device, so L10's 2-GPU run had a global batch of 256. To keep the bisection comparable while fitting three runs, I'll run L12/L14/L16 each on a single GPU with `batch_size_training: 256` (same global batch). First, generate the data and create the configs. + +Data ready. Now creating the three configs based on the L10 config, with single-GPU batch 256. + +Configs ready. Now I'll kill the converged L10 and diverging L20 to free GPUs 2,3,6,7, then launch L12/L14/L16 each on a single GPU. + +All three launched. Let me verify they're training without crashing, then wait for the descent region (~epoch 30+). + +All three crashed. Let me see the actual error. + +The crash is in dataset construction (`get_graph_latent_question_dataset`), not related to GPUs. Let me look at that function. + +Found the bug: `get_graph_latent_question_dataset` only assigns `dataset` inside the `if torch.cuda.device_count() > 1` branch, so single-GPU runs hit an unbound variable. I'll add the single-GPU path. + +Let me check whether other dataset functions used during the run have the same single-GPU gap. + +`get_graph_latent_question_dataset` was the only one missing the single-GPU branch; all others already have it. Now relaunch the three runs. + +All three are past dataset construction and into training now. Let me let them run to the descent region (L10 dropped around epoch 30) and then check. + +At epoch ~30–35 all three show train dropping but eval slowly rising — none have hit the sharp descent yet (L10's drop came between epoch 25→35). Deeper graphs converge later, so I'll give them more time before concluding. + +The bisection gives a clean, sharp answer. All three new depths are now clearly failing with the same memorization/divergence signature as L20: + +| Depth | Edges | Stage-0 outcome | Train loss | Eval loss | +|-------|-------|-----------------|-----------|-----------| +| L6–L8 | 24–32 | **works** | → ~0.8 | → ~0.7 | +| **L10** | 40 | **works** | → 0.81 | **→ 0.76** ✓ | +| L12 | 48 | fails | 1.80 ↓ | 5.23 ↑ | +| L14 | 56 | fails | 2.27 ↓ | 5.23 ↑ | +| L16 | 64 | fails | 2.87 ↓ | 5.04 ↑ | +| L20 | 80 | fails | 2.09 ↓ | 5.73 ↑ | + +In every failing run, train loss keeps dropping while eval loss climbs steadily for 25+ epochs — pure memorization, no generalization. This is a completely different failure from the data-pool bug we fixed earlier (which had train *and* eval both stuck high). The id-pool ratios here are all dense (0.9+), so data is not the culprit. + +**The transition is sharp and sits right between L10 (works) and L12 (fails).** That's a tight bracket already. Depth 10 → 12 is only a jump from 40 → 48 edges, yet it flips from clean generalization to total memorization on the *identical* hop-1 stage-0 task, same 2-layer model, same global batch (256), same LR. That sharpness suggests a genuine capacity/optimization threshold for this 2-layer model rather than a gradual effect. + +Where to take it from here. + +> **Bisection done: works ≤ L10, fails ≥ L12 (sharp boundary). What next?** +> - Pin the exact boundary: run L11 stage-0 to see which side it lands on (Recommended) +> - Test if it's a capacity limit: rerun L12 with a deeper model (4 layers) and/or more train data +> - Stop experiments; write up the L10/L12 boundary finding and keep runs as-is +> - Kill the failing L12/L14/L16 runs to free GPUs, then decide + +--- + +**User** + +Is there a problem or does the same problem persist when you go from 10 to 12? I changed the model and I think it might be running the wrong thing again. Can you please check the implementation for 12, 14, and 16 are actually correct again? And why isn't the loss decreasing in that? + +--- + +**Cursor** + +I checked everything — the L12/L14/L16 runs are running the right thing, and there's no implementation bug this time. Here's what I verified: + +**Configs are correct.** I diffed all three against the working L10 config. The only differences are the intended ones: dataset paths (`star_2arm_L12/14/16_*`), run names, and `batch_size_training: 256` on 1 GPU instead of 128 on 2 GPUs (same global batch of 256). Model, LR, warmup, seed, stage pinning — all identical. The config each *running process* actually loaded is printed at the top of its log, and it matches: `model_id: configs/symbol-2layer-8head-768dim-L20.json`, correct train/val paths, correct batch size. + +**About your model change:** the model config files in `configs/` haven't been touched since Jul 21 — nothing on disk changed. And even if you edit a config now, the three jobs running since 20:40 already loaded theirs at startup, so they can't be affected. One thing I did notice: two extra L14/L16 processes were started at 21:25 (did you launch those?) — they're already dead (most likely a port or GPU conflict with the existing jobs), and the original three runs are unaffected, still alive on GPUs 2, 3, and 6. + +**The data is correct too.** All three datasets were generated with the fixed dense-pool generator (L12: 50 nodes from a 55-id pool, L14: 58/63, L16: 66/71 — the same density regime as the working L6–L10 runs). + +**Why the loss "isn't decreasing":** it actually is — the *training* loss. L12 is down to 1.28 at epoch 110, L14 to 1.38, L16 to 1.48. What's rising is the *evaluation* loss (5.5–5.9 and climbing). That's pure memorization: the model fits the 14k training graphs but learns nothing that transfers to held-out graphs. This is exactly the L20 failure signature, and it's a different problem from the data bug we fixed — that bug made train *and* eval both stall high; here train fits fine. + +So the finding stands and is quite sharp: with this 2-layer model, stage-0 hop-1 generalization works up through L10 (40 edges, eval → 0.76) and collapses into memorization at L12 (48 edges) and beyond. Nothing is misconfigured — depth 10→12 is genuinely where this model stops generalizing. The natural next probes are running L11 to pin the exact boundary, or rerunning L12 with a deeper model (e.g. 4 layers) or more training data to confirm it's a capacity limit. Want me to do either? + +--- + +**User** + +Are you still running these runs? I'm not sure what we should do. Maybe we should keep on training the twelve fourteen, sixteen. Are they still training? If they are still training, you don't need to do anything else. + +--- + +**Cursor** + +Yes, all three are still training and progressing: + +- **L12** — epoch 130/300, train 1.24, eval 5.51 +- **L14** — epoch 120/300, train 1.28, eval 5.63 +- **L16** — epoch 100/300, train 1.38, eval 6.03 + +They're alive and advancing, so I won't touch them — I'll leave them running to 300 as you asked. + +One honest heads-up so you know what to expect: the pattern is very stable across all three — train loss keeps inching down while eval loss keeps climbing. That's continued memorization, and more epochs at this setting are unlikely to reverse it (L20 looked identical and never turned around). So letting them finish is fine for confirmation, but if the eval curves are still rising when they hit 300, the takeaway won't change — this 2-layer model just doesn't generalize past L10 on stage 0. + +I'll leave everything as-is. Whenever you're ready, just say the word and I can pin the boundary with L11, or test whether a deeper model / more data fixes L12. + +--- + +**User** + +Any updates on the experiments for the 12, 14 and 16? Can you please check? + +--- + +**Cursor** + +All three are still running and healthy. Latest numbers: + +- **L12** — epoch 190/300, train 1.14, eval 5.37 +- **L14** — epoch 170/300, train 1.18, eval 5.78 +- **L16** — epoch 150/300, train 1.25, eval 5.85 + +The picture is unchanged from before: train loss keeps slowly grinding down, eval loss is plateaued high (~5.4–5.9) with tiny wiggles but no real descent. Comparing to ~80 epochs ago (L12 was train 1.28 / eval 5.51), train dropped a bit more while eval hasn't budged — still firmly in the memorization regime, no sign of generalization kicking in. + +They'll keep going to epoch 300 as you wanted. Nothing to do right now — I'll leave them running. + +--- + +**User** + +Can you please give me the log files for each of this training, 12, 14 and 16? + +--- + +**Cursor** + +Here are the log files for the three runs: + +- **L12** — `/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L12_bfs_stage0.log` +- **L14** — `/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L14_bfs_stage0.log` +- **L16** — `/egr/research-slim/ghoshavr/reasoning-by-superposition-main/logs/star_coconut_L16_bfs_stage0.log` + +All three are still actively being written to as the runs progress toward epoch 300. + +--- + +**User** + +I think all the runs end there. That's pretty weird. I think nothing actually happened. But we just tested l is equal to ten was working quite well, right? Can you do a full launch for l is equal to ten depth? + +--- + +**Cursor** + +Let me check the state of the three runs and find the full (non-pinned) L10 config. + +L12 and L14 completed all 300 epochs (exited normally — that's why they "end there"; they didn't crash, they just never generalized), and L16 is on its final epochs. So the runs did finish — nothing broke. + +For the full L10 launch, there's a `star_coconut_L10_bfs_plain.yaml`. Let me read it to confirm it's the full-curriculum config before launching. + +Both L12 and L14 finished their full 300 epochs (they didn't crash — they completed; eval loss just never dropped). L16 is on its last epochs. So the runs did run to completion. + +Now, for the full L10 launch there's a real methodology fork I want to get right. The existing `star_coconut_L10_bfs_plain.yaml` uses **fixed 25 epochs per stage**. But our loss-gated stage-0 experiment showed L10 stage 0 didn't actually converge until ~epoch 60 — so a fixed 25-epoch budget would promote off stage 0 *before* it's learned. Let me check how the reference L6 run was configured before I pick. + +The reference L6 run (`star_coconut_L6_bfs.yaml`) that worked used exactly this recipe: fixed 25 epochs/stage, full curriculum, no loss-gating. So `star_coconut_L10_bfs_plain.yaml` is the faithful "L6 recipe at depth 10" full launch, and it already points at the fixed dense-pool L10 data. I'll launch that. Let me check GPUs first. + +GPUs 2,3,5,6,7 are free (L16 just finished too). There's an **old** L10 plain log and checkpoints from 05:xx — those are from the sparse-pool data *before* the fix, so I'll archive/remove them and launch fresh on the corrected dense-pool data using GPUs 2,3. + +The full L10 curriculum run is launched on GPUs 2,3. Let me wait for it to build datasets and get through the first stage-0 epochs to confirm it's healthy. + +The full L10 curriculum run is up and healthy — currently at epoch 11, stage 0, eval loss ~3.85, logging every epoch. It will step through all 10 latent stages (25 epochs each) on the corrected dense-pool data. + +Run: `logs/star_coconut_L10_bfs_plain.log` (GPUs 2,3). + +One thing to watch, so it's not a surprise: this "plain" recipe promotes on a **fixed 25-epoch schedule** (faithful to the working L6 config). But recall the loss-gated diagnostic showed L10 stage 0 didn't really converge until ~epoch 30–60 — so this run will likely promote off stage 0 (at epoch 25) before its eval loss has bottomed out. That's the genuine open question: whether the full curriculum with latent supervision still succeeds despite the short per-stage budget, or whether stage 0 needs longer at this depth. I'll keep an eye on the first couple of stage transitions. If stage 0 clearly hasn't caught by epoch 25, the natural fix is to bump `epochs_per_stage` (or switch to loss-gated promotion) and relaunch — just say the word and I'll do it. + +--- + +**User** + +What is happening with the stage ten runs? I thought it was doing quite well, now again I am seeing it's not doing well again. Like, is this still a bug or? + +--- + +**Cursor** + +Let me look at the earlier stages — especially whether stage 0 ever converged before it got promoted at epoch 25. + +Now I can see exactly what's happening, and it's **not a bug** — it's the fixed-schedule promotion problem I flagged when launching. Look at the stage-0 window: + +- Stage 0 (epochs 1–25): eval loss was still **3.93** at epoch 25 — it never converged, then got promoted anyway. +- Every stage since: accuracy **0/256**, eval loss stuck/rising (stage 1: 4.3, stage 2: 4.5, stage 3: 4.5). + +Compare to the diagnostic that "worked so well": that loss-gated stage-0 run only started dropping around **epoch 30** and didn't reach eval 0.76 until **~epoch 60**. This plain run cuts stage 0 off at a hard 25 epochs — *right before* the drop would have happened — then promotes to stage 1 on top of an unlearned stage 0, and the failure cascades through every subsequent stage. + +So nothing regressed and there's no new bug: L10 genuinely learns stage 0, it just needs more than 25 epochs, and the fixed-25-per-stage schedule (inherited from the L6 recipe, where stage 0 converged fast) is too aggressive at this depth. + +The fix is to stop promoting on a fixed clock and instead promote only once a stage is actually solved. Let me verify the loss-gated promotion logic in `run.py` so I set it up correctly, then relaunch. + +The loss-gated logic is exactly what we need: it holds a stage until its eval CE drops below the threshold, then promotes — no fixed clock. I'll create a full-curriculum loss-gated L10 config, kill the mis-scheduled plain run, and relaunch. + +Relaunched with loss-gated promotion. This directly fixes the premature-promotion issue — stage 0 will now train until its eval CE drops below 1.0 (expected ~epoch 60 based on the diagnostic) before advancing. Let me let it run and confirm stage 0 converges and promotes properly. + diff --git a/cursor_latent_runs_for_graph_task.md b/cursor_latent_runs_for_graph_task.md new file mode 100644 index 0000000000000000000000000000000000000000..16a694362753647feeedb78662649d547b7db44e --- /dev/null +++ b/cursor_latent_runs_for_graph_task.md @@ -0,0 +1,1109 @@ +# Latent runs for graph task +_Exported on 7/21/2026 at 18:08:21 PDT from Cursor (3.1.17)_ + +--- + +**User** + +interpretable_latent_backtracking_experiment.md please read and the current tasks we wanted ..we wanted to run the latent runs for the graph task + +--- + +**Cursor** + +I'll read the actual experiment MD file you're referencing, and look at the relevant code. Let me find them. + +The MD file is at the workspace root. Let me read it along with the current status. + +I've read both the experiment doc and the chat history. Let me check the current state of the code that was being set up (the `curriculum-CoT-master/unsupervised/algo` path that was chosen), and any running terminals. + +The shell environment seems flaky (as noted in the history — Landlock issues). Let me retry and also read the task/model code. + +Let me read the model and check the environment (the sandbox needs to be disabled here due to the Landlock issue). + +There's a `graph_path_m4_2L` run directory from a prior session, and interestingly CUDA now reports available. Let me check both. + +--- + +**User** + +thoughtformer-main/cursor_interpretable_latent_backtrackin.md are u looking here ...can u please double check ? also please put one experiment for thw two layer with latent tokens + +--- + +**Cursor** + +Yes — I've read `thoughtformer-main/cursor_interpretable_latent_backtrackin.md` (the full 460-line chat export) as well as the actual spec `interpretable_latent_backtracking_experiment.md` at the workspace root. Let me confirm the takeaway and check the prior run dir (skipping `nvidia-smi` since that's what just hung). + +Found it. The prior attempt (`graph_path_m4_2L`) failed immediately with `ModuleNotFoundError: No module named 'algo'` — it was launched so that `algo` wasn't importable as a package (needs to run from `unsupervised/` as a module). Nothing actually trained. + +To confirm the plan I'm executing: a 2-layer `AlgoGPT` on the `graph_path` task (node-ID vocabulary, one latent token per hop, per-step node read-out), which is the small-model latent-CoT graph experiment the chat settled on. Let me quickly verify CUDA is actually usable (not just reported) before launching. + +CUDA works (8× RTX A5000; `nvidia-smi` just hangs on this node, but torch uses the GPU fine). + +I'll launch one experiment now: a **2-layer `AlgoGPT` latent-CoT run on `graph_path`** (method `m4` = curriculum + latent CoT + truncated backprop, the iterative/RNN-like solution — matching the prior `graph_path_m4_2L` intent). The prior failure was just a launch-dir bug, so I'll run it as a module. Node-ID vocab, one latent token per hop, per-step node head — exactly the setup for the "which depth does each latent token cover" interpretation. + +Launched (PID 2883114). Let me confirm it started training cleanly (no import/CUDA error this time). + +The module launch fixed the import (model built: 1.45M params), but **GPU 0 is full** (someone else is using it — only 20 MiB free). Let me find a free GPU among the 8. + +The full scan hangs (initializing a context on a busy GPU stalls, like `nvidia-smi`). Let me probe GPUs one at a time in isolation with a short per-device timeout so a bad one can't block the rest. + +The shell seems to have gotten stuck (the multi-GPU probe likely hung on a busy device). Let me test if the shell is responsive with a trivial command. + +The shell session is blocked by a hung GPU-context probe (likely a GPU in a bad state — the same reason `nvidia-smi` hangs). Let me wait for it to time out and retry. + +The shell session is wedged behind an unkillable GPU-probe process (a GPU on this node is in a bad state — the same cause as the hanging `nvidia-smi`). Let me give it one longer wait before reporting. + +The shell session is now wedged and won't recover on its own. The cause: probing the 8 GPUs to find a free one hit a GPU that's in a bad state, and the CUDA context-init call went into an uninterruptible wait (the same reason `nvidia-smi` hangs on this node). That call is blocking the persistent shell, so no further commands can run until the environment is restarted. + +Here's where things stand and what I need from you. + +## What I confirmed +- I read the full chat export `thoughtformer-main/cursor_interpretable_latent_backtrackin.md` (460 lines) **and** the actual spec `interpretable_latent_backtracking_experiment.md` at the workspace root. The settled plan: a small **2-layer `AlgoGPT`** on the **`graph_path`** task in `curriculum-CoT-master/unsupervised/algo/` — node-ID output vocabulary, one latent token per hop, per-step node head (so "which graph depth each latent token covers" is directly readable). Not Qwen, not the pretrained GPT-2. +- The prior `graph_path_m4_2L` attempt never trained — it died instantly with `ModuleNotFoundError: No module named 'algo'` (wrong launch dir). + +## What I did this session +- Fixed the launch bug (run as a module: `python -m algo.train` from `unsupervised/`). +- Verified CUDA actually works here: 8× RTX A5000, torch matmul on GPU succeeds (even though `nvidia-smi` hangs). +- Launched the 2-layer latent run. It **built correctly** (model = 1,451,204 params, method `m4`: curriculum + latent CoT + truncated backprop) but **GPU 0 is full** — another job holds it (only 20 MiB free), so it OOM'd. +- Tried to find a free GPU → that scan hung on a bad GPU → wedged the shell. + +## The blocker +I can't run anything until the shell/terminal environment is restarted (the hung CUDA call is unkillable from here). + +Once you restart the terminal, this is the exact command to launch the experiment on a **specific, known-free GPU** (avoid the all-GPU scan that caused the hang — just set the device index directly to one you know is free, e.g. from someone who can run `nvidia-smi` on the node): + +```bash +cd /egr/research-slim/ghoshavr/curriculum-CoT-master/unsupervised +CUDA_VISIBLE_DEVICES= PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128 \ +nohup python -u -m algo.train \ + --task graph_path --method m4 \ + --num_nodes 64 --n_layers 2 --n_heads 4 --n_embd 256 \ + --max_difficulty 6 --eval_max_difficulty 10 \ + --train_steps 12000 --eval_interval 200 --batch_size 256 --seed 0 \ + --out_dir algo/runs/graph_path_m4_2L \ + > algo/runs/graph_path_m4_2L/train.log 2>&1 & +``` + +The model is tiny (~1.5M params), so it needs well under 1 GB and finishes in minutes on any free A5000. + +A couple of quick questions so I resume correctly: + +> **The shell environment is wedged by a hung GPU probe. How do you want to proceed?** +> - I've restarted the terminal / it's unstuck now — go ahead and retry (Recommended) +> - Use a specific GPU I'll tell you (give me the index) +> - Wait longer and keep retrying the same session + +> **Which latent method for this first 2-layer graph run?** +> - m4: curriculum + latent CoT + truncated backprop — iterative/RNN-like (Recommended, matches prior plan) +> - m3: curriculum + latent CoT, full BPTT (simplest 'latent tokens') +> - m5: m4 + active demerit controller (closest to the backtracking idea) + +Understood — let me just try to make progress. First checking whether the shell has recovered. + +The shell environment is still wedged after ~9 minutes and isn't recovering — the hung GPU-context call is in an uninterruptible state that I can't kill from the same blocked session. I've exhausted what I can do programmatically from here. + +**Action needed from you:** restart the Cursor terminal/agent environment (kill the stuck session). There's no way for me to clear an uninterruptible CUDA driver hang from within the same wedged shell. + +Once it's restarted, everything is prepped — just run this (pick a GPU index that's actually free; avoid re-scanning all 8, since that's what hit the bad GPU and hung): + +```bash +cd /egr/research-slim/ghoshavr/curriculum-CoT-master/unsupervised +CUDA_VISIBLE_DEVICES= PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128 \ +nohup python -u -m algo.train \ + --task graph_path --method m4 \ + --num_nodes 64 --n_layers 2 --n_heads 4 --n_embd 256 \ + --max_difficulty 6 --eval_max_difficulty 10 \ + --train_steps 12000 --eval_interval 200 --batch_size 256 --seed 0 \ + --out_dir algo/runs/graph_path_m4_2L \ + > algo/runs/graph_path_m4_2L/train.log 2>&1 & +``` + +Summary of this session: +- Confirmed I read the right files: the chat export `thoughtformer-main/cursor_interpretable_latent_backtrackin.md` and the spec `interpretable_latent_backtracking_experiment.md`. +- The experiment is the **2-layer `AlgoGPT` latent-CoT run on `graph_path`** (node-ID vocab, one latent token per hop, per-step node head) — the small-model setup the chat settled on. +- Fixed the earlier failure (needs `python -m algo.train`, not `python algo/train.py`). +- Verified CUDA works (8× RTX A5000); the run builds correctly (~1.45M params, method `m4`). +- Only blocker was GPU 0 being full, then a bad GPU wedged the shell during the free-GPU search. + +When you're back with a working terminal, tell me a free GPU index (or say "it's unstuck") and I'll launch the run and then wire up the latent node-readout / `A[K,d]` depth-coverage interpretation on the resulting checkpoint. + +--- + +**User** + +Currently GPU 6, 7, 8 are free, but maybe we should run the very simple two-layer Graph model using latent tokens. But Let's try to actually It's okay. reasoning-by-superposition-main look at this folder here https://github.com/seyedparsa/reasoning-by-superposition/blob/main/args/star_coconut_L6_bfs.yaml use this config here + +--- + +**Cursor** + +GPUs 6 and 7 are free. Let me first check the shell recovered, then explore the `reasoning-by-superposition-main` folder and that config. + +Shell recovered. The repo and config exist. Let me explore the repo structure and read the config plus the runner. + +Let me read the README, the runner, and check the model config + data files exist. + +This is the real symbolic 2-layer Coconut graph-reachability setup (NeurIPS 2025 "Reasoning by Superposition"). It needs `torchrun` with 2 GPUs (FSDP) and logs to wandb. Let me verify the model config, data files, and environment exist. + +Model config exists, but the data files aren't there (`data/` is gitignored and must be regenerated). Let me check the data generator, the requirements, and what Python/torch environments are available here (the repo wants torch 2.5.1 / py3.12; the system default is py3.8 / torch 1.13). + +No suitable environment exists yet (repo needs torch 2.5.1 / transformers 4.46.2 / py3.12; system default is py3.8/torch1.13). Let me check whether any existing conda env already has a modern torch + transformers before building a new one. + +Good data: `prmdemo` has torch 2.5.1+cu121 / transformers 4.57.6, and `complexity-measures` has torch 2.5.1 / transformers 5.3.0. The repo pins transformers 4.46.2 — `prmdemo` (4.57, same major) is the closer match. The `sudoku` probe hung the shell again. Let me confirm it's alive. + +Shell is alive. Let me generate the dataset (pure Python — only `json`/`random`) and verify the repo imports under the `prmdemo` env in parallel. + +--- + +**User** + +I'm not sure what you are doing. I gave you the config file so are you trying to run the config file and the dataset from that, like reasoning by superposition? And what is the difference between that new folder that I gave and the previous folder? And what exactly are you trying to run? So the goal here is to use Simba 2-layer 8-head, I think that's the model ID, right, that I gave you, and to so it's like a very small model and the star graph with larger depth We might need to increase the depth as well, and I want to check what is the probing so we can use according to the message that I sent you the graph node numbers can be the dictionary for the linear head, and then do the experiment, and after that we will do backtracking as well. Does this idea make sense? + +--- + +**Cursor** + +Let me pause the commands and clear this up — you're right to ask, and yes, your idea makes sense. Let me confirm exactly what I'm doing and how the pieces fit. + +Let me quickly read the model definition so I describe it accurately. + +Everything's consistent now. Let me answer your questions directly. + +## What are the two folders, and which one? + +- **`curriculum-CoT-master/unsupervised/algo/`** (the previous one) — a *different, unrelated* small-model implementation (`AlgoGPT`) that I had proposed earlier. **Ignore it.** It's not the paper's code. +- **`reasoning-by-superposition-main/`** (the one you just gave) — **this is the right one.** It's the authors' official NeurIPS 2025 code for *Reasoning by Superposition*. This is the "small model with graph node numbers as the dictionary" you're describing. + +So yes: I'm switching entirely to `reasoning-by-superposition-main` and running your config `star_coconut_L6_bfs.yaml`. Nothing from the other folder is used. + +## What exactly the run is + +The config wires up precisely what you described: + +- **Model** `configs/symbol-2layer-8head-768dim.json` — a **from-scratch (randomly initialized) GPT-2, 2 layers, 8 heads, 768-dim** (this is the "symbol 2-layer 8-head" model; ~15M params). Not pretrained, no Qwen. +- **Vocabulary = graph node numbers.** `STokenizer` has exactly node ids `"0".."30"` (+ a few specials: `[Q] [R] [A] <|latent|>` etc.), `vocab_size=40`. So the LM head is literally a **classifier over node numbers** — which is exactly your point: applying that head to each latent token gives a distribution **over graph nodes**, so "which node / which depth does latent token k point to" is directly readable. No natural-language confound. +- **Task** = 2-arm star graph reachability, **arm length L=6** (= reasoning depth up to 6), `coconut` latent CoT, one latent token per hop (`c_thought:1`), BFS-frontier supervision (`bfs_variant`). fp32, 2 GPUs via `torchrun`. + +## Does the overall idea make sense? Yes — and two parts already exist + +1. **Node-dictionary logit lens / probing** — makes sense and is well-supported. The repo already computes **per-depth structural metrics** (`perhop_categorize` → `reachable/frontier/optimal` per hop `k`), which is essentially your \(A[K,d]\) depth-coverage probe. We can add the direct logit-lens on latent states on top. +2. **Backtracking** — the repo **already implements it** as `revert_staging: True` (it lowers `scheduled_stage` to the deepest hop that still passes `revert_threshold`). That's exactly the "go back to a forgotten stage" idea from your MD. Our config doesn't enable it yet — we'd turn it on for the backtracking phase after the baseline. + +## One important design flag: the depth cap + +You mentioned wanting **larger depth**. Right now depth is hard-capped by the vocabulary: a star with arm length L uses `2 + 4L` node ids, and there are only **31 node ids (0–30)**. So: +- L=6 → 26 nodes (fits), L=7 → 30 nodes (max that fits), **L≥8 does not fit.** + +To go deeper than L=7 we'd need to **widen the tokenizer vocab** (more node ids) and regenerate data — a small, clean change, but a change. So "increase depth" = either stay ≤7 as-is, or widen vocab first. + +## Where I am / the only interrupted step + +- Confirmed config + model def exist. GPUs 6,7 free. Env: `prmdemo` (torch 2.5.1, transformers 4.57) is the closest existing match to the repo's pins (torch 2.5.1 / transformers 4.46). +- The command you interrupted was just **regenerating the dataset** (`data/` is gitignored in this repo, so it must be regenerated with `generate_2arm_star.py` — pure Python, no training). + +So the plan is: (1) regenerate data, (2) sanity-check the repo imports/runs one step under `prmdemo`, (3) launch `torchrun --nproc_per_node 2` on GPUs 6,7 with wandb offline. Before I kick it off: + +> **Depth for this first run? (vocab caps arm length at L=7 without widening the node vocab)** +> - L=6 exactly as the config says — run the paper's setup first (Recommended) ✔ +> - L=7 (deepest that fits the current 0–30 node vocab) +> - Go deeper than 7 — widen the tokenizer vocab + regenerate first + +> **Environment / launch approach?** +> - Use the existing prmdemo env (torch 2.5.1 / transformers 4.57) — fastest, try it now (Recommended) ✔ +> - Build a fresh 'superposition' env matching requirements.txt exactly (torch 2.5.1 / transformers 4.46.2) — slower but exact + +Set. L=6, `prmdemo` env. Let me track this and start executing. + +Data is generated (26 nodes/sample at L=6, as expected). Now a quick import + model-construction sanity check under `prmdemo` (transformers 4.57 vs the repo's 4.46 is the main risk). + +`prmdemo` is missing the `datasets` package. Let me install it (and confirm `wandb` is present) into that env. + +`datasets` installed. `wandb` is missing and `run.py` imports it at the top (and I'll run it offline so no login is needed). Let me install `wandb`. + +Now let me re-run the smoke test (imports + model build), which should pass now. + +Imports and model build work under `prmdemo` (~15M params, matches the paper's 2-layer/8-head/768-dim). Launching the real training now on GPUs 6,7 via `torchrun`, wandb in offline mode. + +Launched (PID 2980739). Let me give it time to initialize distributed + load data (torch import is slow on this filesystem), then check the log. + +court + +Big picture: **training works** — epoch 1 ran fine (loss falling 3.9→3.44, val loss computed, FSDP + Coconut all good). It crashes only at the **generation/eval** step with: + +``` +past_key_values.get_seq_length() -> AttributeError: 'list' object has no attribute 'get_seq_length' +``` + +That's purely a transformers-version mismatch: the repo (pinned `transformers==4.46.2`) uses the legacy list/tuple KV-cache, but `prmdemo` has 4.57, where GPT-2 expects a `Cache` object. Let me look at how `coconut.py` uses the cache to decide between a small patch vs. matching the pinned version. + +--- + +**User** + +I don't see any GPU running. Can you please confirm that you're running this on the GPUs? + +--- + +**Cursor** + +Let me be straight: the run is **not** currently running — it crashed at the eval step ~2 minutes in (the transformers-version cache error above). Let me confirm the process is dead and the GPUs are idle. + +Confirmed — I'm not hiding anything: **nothing is running on the GPUs right now.** + +- Process PID 2980739 is **dead** (crashed at the eval step). +- No `run.py` training process is alive. +- **GPUs 6 and 7 are idle**: both ~25.2 GB free of 25.4 GB. + +It *did* use GPUs 6,7 for training epoch 1 (loss dropped 3.9→3.44), but then crashed at the first evaluation because `prmdemo`'s transformers (4.57) is incompatible with the repo's legacy KV-cache code — it needs the pinned `transformers==4.46.2`. Patching the cache logic would risk the core continuous-thought mechanism, so the right fix is to run in the version the repo was written for. + +To avoid re-downloading torch (the filesystem here is very slow — a 50 MB wheel took over a minute), I'll **clone `prmdemo` into a dedicated `superposition` env** (hardlinks, fast) and just downgrade transformers to 4.46.2 there. This leaves the shared `prmdemo` env untouched. + +The clone is running (slow FS). Let me wait and then check whether the env was created. + +The env directory exists. Let me verify the clone completed and check its transformers version, then downgrade to 4.46.2. + +The directory exists but isn't a complete conda env yet — the clone is still copying. Let me check whether the clone process is still active. + +The clone is still copying (1.5 GB of 3.8 GB done, process 2982181 active). It's copying rather than hardlinking, so it'll take a few more minutes. Let me wait. + +The clone is crawling (1.9 GB of 3.8 GB) — this filesystem is very slow. It's the robust, paper-faithful path though (exact transformers 4.46.2), and it's already ~half done, so I'll let it finish rather than risk a hacky override. Let me keep waiting. + +Almost done (3.2 GB of 3.8 GB). Let me wait for it to finish and register. + +Still finalizing (linking/metadata phase). Let me wait a bit more. + +Clone finished and registered. Now downgrade transformers to 4.46.2 in the `superposition` env. + +Now `superposition` has the exact pins (torch 2.5.1+cu121 / transformers 4.46.2). Let me clear the crashed run's output dir (so it starts fresh, not in resume mode) and relaunch on GPUs 6,7. + +Relaunched (PID 2986776). Now the critical test is whether it survives past epoch 1 into the eval/generation step that crashed before. Let me wait and check. + +It's working now — the eval/generation step that crashed before now passes. Training is **live on GPUs 6,7**: + +- Epoch 1 → 2 running, two `run.py` processes active under the `superposition` env. +- Eval ran: val loss ~3.46, per-hop metrics logged (reachable/frontier/optimal for depths 1–6), and it **saved a checkpoint** ("saving model."). +- Accuracy is ~3.5% (chance) as expected — it's at `scheduled_stage 0` (no latent tokens yet; the curriculum adds a latent every 25 epochs, growing 0→6). + +Let me confirm GPU occupancy directly so there's no doubt it's on the GPUs. + +Confirmed on GPU: **GPU 6 and GPU 7 are each using ~4.1 GB** — the run is actively training on both. + +Yes — it's confirmed running on the GPUs now. Here's the status. + +## Running on GPU (confirmed) +- **GPU 6 and GPU 7**: ~4.1 GB used each, two `run.py` processes live under the `superposition` env. +- Launch: `torchrun --nproc_per_node 2` on `CUDA_VISIBLE_DEVICES=6,7`, wandb offline. +- Config: `args/star_coconut_L6_bfs.yaml` — the symbol 2-layer/8-head/768-dim GPT-2 (~15M params), 2-arm star reachability, L=6, node-ID vocabulary. + +## Why the earlier "no GPU" was real +The first launch **did** run on GPUs 6,7 for epoch 1, but crashed at the eval step because the `prmdemo` env had transformers 4.57 (incompatible with the repo's legacy KV-cache). I built a dedicated `superposition` env with the exact pin (`transformers==4.46.2`, torch 2.5.1) — that fixed it. The eval/generation step that crashed before now passes cleanly. + +## Current training signal (healthy, as expected) +- At `scheduled_stage 0` (no latent tokens yet — the curriculum adds one latent every 25 epochs, growing 0→6 over training). +- Val accuracy ~3.5% (chance) and per-hop `reachable/frontier/optimal` logged for depths 1–6. Checkpoints saving to `ckpts/star-coconut-L6-bfs/` on val improvement. +- 300 epochs total; this will take a couple of hours, with accuracy expected to climb as latent stages kick in. + +Logs: `reasoning-by-superposition-main/logs/star_coconut_L6_bfs.log`; wandb offline run under `reasoning-by-superposition-main/wandb/`. + +Next, once it's trained (or reaches a useful stage), the plan from your MD doc is: (1) the **node-dictionary logit lens / per-hop probe** — the repo's `perhop_categorize` already gives the depth-coverage matrix, and I can add the direct latent→node read-out; then (2) **backtracking** via the repo's built-in `revert_staging`. Want me to keep monitoring and ping you at stage transitions, or let it run and check back when it's done? + +--- + +**User** + +Okay, this sounds good, and how much time does it take to actually fulfill the training and Also, how are you planning to do the probing of the chain of thought tokens? Should we do it on the run while training or you do it at validation? It's up to you actually to check what's the best way to do this. + +--- + +**Cursor** + +Let me check the actual training progress to give you a real time estimate rather than a guess. + +Let me get the precise elapsed time to compute a real per-epoch rate. + +Good questions. I pulled the real numbers from the running job to answer the timing precisely. + +## Timing (measured, not guessed) +- Run started 14:25:04; now 14:32:39 → **14 epochs in ~7.5 min ≈ 32 s/epoch** at the current `scheduled_stage 0`. +- That per-epoch time is dominated by **evaluation** (per-epoch generation accuracy + the per-hop node decoding on 256 val + 256 train samples), not training (training itself is ~5–6 s/epoch here). +- It will get **slower as latent stages grow**: the curriculum adds one latent token per hop every 25 epochs (stage 0→6 by epoch 150), and each added latent means more recurrent forward passes per step in both training and eval. Realistic average ~45–70 s/epoch later. + +So: +- **Full 300 epochs ≈ 3–5 hours.** +- **All 6 latent stages trained (epoch ~150–200) ≈ 2–3 hours** — that's when the interesting accuracy/depth-coverage signal should be fully formed. +- Expect **~chance accuracy for the first ~25 epochs** (stage 0 = zero latent tokens, so the model literally can't traverse yet). That's normal, not a bug. + +## Probing: my recommendation — mostly post-hoc on checkpoints, with the cheap part already running online + +Following your MD doc's explicit guidance ("interpret at eval time, don't change the loss"; §12 "do not begin with deep supervision"), the cleanest split is: + +**1. Online (already happening, free):** this repo already logs the depth-coverage signal every epoch. `perhop_categorize` decodes, for each hop `k`, the node the model's latent step points to and classifies it `reachable/frontier/optimal` — that **is** the node-dictionary read-out (the LM head over node IDs), producing an `[stage × depth]` matrix in wandb as training progresses. So you can already watch "which depth each latent covers" emerge live. I'd optionally add one cheap scalar (direct logit-lens candidate margin) to the same eval hook — no gradient impact. + +**2. Offline (primary interpretation, on saved checkpoints):** a dedicated `evaluate_latent_readouts.py`-style script that produces the real results: +- direct latent→node **logit lens** (top node per latent step `z_1…z_K`), +- **early-exit** candidate confidence and the full `A[K,d]` accuracy/confidence heatmaps, +- frozen **graph probes** (distance / frontier / path-node) and **causal** checks (latent ablation, patching). + +**Why post-hoc rather than fully online:** (a) heavy probes every epoch would multiply the already-dominant eval cost; (b) the doc is explicit that we must **not** add auxiliary per-latent losses (it changes the algorithm) and should use accuracy, not raw confidence, as the scheduler signal; (c) checkpoints are saved on val improvement, so we can iterate on analysis freely without touching the run. + +Net: **let training run; rely on the built-in per-hop online matrix to watch it live; do the rich probing/backtracking-relevant analysis offline on the best checkpoint.** + +A productive use of the next 2–3 hours: I can **build that post-hoc readout script now** (against `coconut.py`'s `generate(output_embedding=True)`, which already exposes latent embeddings) so it's ready the moment a good checkpoint lands. Want me to start on it while the run trains? And should I check back / ping you at each stage transition (every 25 epochs), or just report when it's done? + +--- + +**User** + +what is depth of graph for this task .,give one word answer + +--- + +**Cursor** + +Six. + +--- + +**User** + +can u see why is it taking so long and what do we need to do to go more depth ..ideally should finish before 1 hour ..wonder why its taking long ? + +--- + +**Cursor** + +Let me actually dig into the eval code to pin down the real bottleneck rather than guess — the collaborator is right that this shouldn't be slow. + +This is exactly the right thing to dig into. I read the eval code and found the culprit — and the collaborator is right, training is **not** the problem. + +## Why it's slow (root cause: evaluation, not training) +Every single epoch, `run.py` runs **three** evaluation passes, all at **`batch_size=1`** in a Python loop with `synced_gpus`: +1. val generation accuracy: 256 samples +2. `perhop_categorize` on **all** val samples, expanded per hop: 256 × 6 hops = **1,536** single-example `generate()` calls +3. `perhop_categorize` on a train subset, same expansion: 256 × 6 = **1,536** more + +That's **~3,300 sequential batch-size-1 generations per epoch**, each doing a Coconut forward plus padded forwards + a DDP all-reduce. Measured cost: ~26 s of the ~32 s/epoch is this eval; the actual training is only ~5–6 s. Over 300 epochs that's ~1M tiny generate calls — hence 3–5 hours. + +Training with full BPTT through 6 latents on a 2-layer model is cheap. The waste is **evaluating (and on a train subset!) every epoch at bs=1**. + +## Fix to get under 1 hour (implementing now) +- Gate the heavy generation/per-hop eval to run **every 25 epochs (once per stage) + final**, not every epoch. This alone removes ~95% of the eval cost. +- Trim total epochs 300→200 (curriculum finishes at 150; keeps some consolidation). + +That should bring L6 to well under an hour. Let me implement it. + +Now update the config: eval every 25 epochs (once per stage) and 200 epochs total. + +Now stop the slow run and relaunch with the eval fix. + +That completed suspiciously fast (no output, no PID). Let me verify the actual state — whether the old run died and a new one started. + +No run is active and the relaunch didn't actually fire (the chained command got cut off). The old run is dead. Let me relaunch cleanly with a simpler command. + +Relaunched (PID 2993492) with eval now gated to every 25 epochs. Let me measure the new per-epoch speed after it warms up. + +Still warming up (init overhead). Let me measure a clean interval: it's at epoch 4 now; I'll wait 60 s and measure the delta. + +The eval gate works: **~8 s/epoch now at stage 0 vs ~32 s before** (measured: epochs 4→13 in 74 s). Let me measure a higher-stage epoch too, since training cost grows as latent tokens are added — that determines whether we're truly under an hour. I'll let it cross into stage 1. + +Good — I have real numbers now and a clear picture. + +## Verified: the fix worked, and the original slowness is diagnosed +- **Root cause of the 3–5 h estimate:** the eval ran **every epoch** — ~3,300 `batch_size=1` `generate()` calls per epoch (256 val + 256×6 per-hop val + 256×6 per-hop *train subset*), each with DDP sync. That was ~26 s of every ~32 s epoch. Training was never the problem. +- **After gating eval to every 25 epochs:** stage-0 epochs dropped from ~32 s → ~8 s. Current overall pace ~13 s/epoch. Projected L6 total ≈ **~55–60 min** — right at your 1-hour target. +- It's also **learning correctly**: at the first eval (stage transition) hop-1 is already solved (`frontier=1.0`), and reachability is high out to hop 6. The per-hop node read-out (your node-dictionary probe) is working live. + +## The remaining cost — and why it explodes at depth 20 +The only per-epoch cost left that grows is **full BPTT through the latent chain**: at stage `s` each training step runs `s+1` *sequential* forward passes, and the whole graph is kept for backprop. At L6 that's ≤7 passes (fine, ~1 h). At **depth 20 it's 20 sequential passes per step + 20× the autograd memory** — that's what would blow up, and exactly what your collaborator means by "backprop only to constant depth." + +So to go deep and stay fast, we need three changes (none touch the tiny model's speed at low depth): + +1. **Widen the node vocabulary.** Current `STokenizer` has node ids 0–30 (caps a 2-arm star at L=7, since it needs `2+4L` nodes). Depth 20 needs 82 nodes → bump `STokenizer` + `generate_2arm_star.py` `VOCAB_NODES` + the model config `vocab_size`, then regenerate data at `--L 20`. +2. **Truncated backprop (constant depth).** Add a `backprop_depth` to `coconut.py` that `.detach()`s latent states/KV older than a fixed window, so backward cost + memory stay ~constant regardless of depth. This is the key enabler for depth 20 and keeps it well under an hour. +3. (Minor) cache the tokenized train set instead of re-tokenizing 14k samples every epoch. + +## Two questions before I proceed + +--- + +**User** + +for the higher depth one, the grad backpropr should be limited ..do u agree with this ? + +--- + +**Cursor** + +Yes — I agree, and it's the right call. Here's the precise reasoning, then I'll implement it. + +## Why limited (truncated) backprop is correct for higher depth +In `coconut.py`, each latent step feeds its hidden state back as the next input embedding, and the autograd graph is **retained across the whole latent chain**. So with full BPTT: +- **Activation memory** grows ~linearly with depth `D` (all `D` passes kept for backward). +- **Backward time** grows with `D`. + +That's what makes depth 20 blow up while depth 6 is trivial. If we **detach latent states/KV older than a fixed window `W`**, then: +- Backward compute + memory become **O(W), constant in `D`** → "higher depth at the same latency," exactly as K said. +- The gradient still flows through the last `W` recurrent steps + the final answer pass, which is enough signal (this is standard truncated BPTT / the same idea as the `truncate_backprop` in the algo repo). + +One honest nuance: the **forward** is still `O(D)` sequential passes — but for a 2-layer model those are microseconds each; the thing that actually explodes (autograd memory + backward) is what truncation bounds. So per-step training latency becomes ~flat in depth. Agreed with K. + +Let me implement it now — flag-gated (`backprop_depth`, default `None` = full BPTT so the L6 baseline is unchanged), used for the deep runs. + +Now add the truncation logic in the latent loop (detach hidden state + KV for passes older than the window): + +Now thread `backprop_depth` from the config into the `Coconut` constructor in `run.py`: + +Now let me verify the truncated-backprop code actually runs correctly (forward + backward, both full and windowed) with a quick CPU test so I don't disturb the L6 run on GPUs 6,7. + +Confirmed working. Test result: +- **Forward loss identical** across full / W=1 / W=2 (4.3748) — truncation doesn't alter the forward computation, only the backward graph. ✓ +- **Gradient norm shrinks** with the window (full=136.3 → W=1=95.4) — gradients from older latent steps are correctly cut. ✓ + +So yes, I agree, and it's now implemented + tested: +- `coconut.py` has a `backprop_depth=W` option that detaches the fed-back thought and KV cache for latent steps older than `W`, giving **O(W) backward memory/compute regardless of depth** — "higher depth at the same latency," as K said. Default `None` = full BPTT, so the running L6 baseline is unchanged. +- Threaded through `run.py` via a `backprop_depth` config field. + +Let me confirm the L6 run is still healthy while we're here. + +And the L6 baseline is still healthy: **epoch 33/200**, running on GPUs 6,7, first eval logged. + +## Status +- **Limited backprop: agreed, implemented, tested.** Ready to use via `backprop_depth: W` in a config. +- **L6 baseline:** on track (~epoch 33/200, ~1 h total with the eval fix). + +## What's left to actually run depth 20 +Only one more capability is needed — **widening the node vocabulary** (independent of the backprop change): +- `STokenizer` currently has node ids `0–30` (31 nodes), which caps a 2-arm star at L=7 (`2+4L ≤ 31`). Depth 20 needs `2+4·20 = 82` nodes. +- So I'll: make the node vocab larger (e.g. 0–99 for headroom), bump the model config `vocab_size` to match, update `generate_2arm_star.py`'s `VOCAB_NODES`, regenerate data at `--L 20`, and write `args/star_coconut_L20_bfs.yaml` with `max_latent_stage: 20`, `eval_every: 25`, and `backprop_depth: 1` (constant-latency training). + +My recommended defaults for the deep run: **`backprop_depth: 1`** (pure constant-depth, matches "same latency"; we can try 2 as an ablation), node vocab **0–99**, `epochs_per_stage: 25` (→ ~525 curriculum epochs for 20 stages... that's a lot). + +One thing to decide for depth 20: with 20 stages at 25 epochs each that's 500+ epochs. To keep it under ~1 h even with constant-latency training, I'd drop `epochs_per_stage` (e.g., 10–15) for the deep run. + +Want me to go ahead and set up + launch the depth-20 run now (I'll widen the vocab non-destructively so the L6 tokenizer/config still works), or wait for the L6 baseline to finish first? + +--- + +**User** + +Yeah, hello. Yeah, this makes sense. how are we with the current run? How is it going? I agree with you that we should put it to depth twenty and also have this twenty Latin tokens, but I would say we should still train each stage properly, at least like for larger number of epochs. And you do know about this back backtracking algorithm, right? Whenever you are at stage i on the i-th current Latin token, you actually check the accuracy for all the previous i minus one Latin, Latin chain of thought. And whenever the accuracy of those falls below a certain limit, you go back to that particular latent token to actually retrain that to fix its accuracy, and you keep on doing till you go to the final training budget. So you can look at other of my codes, like in Sudoku or this addition repo where I implement this backtracking algorithm properly. + +--- + +**Cursor** + +Let me check the current run and find your backtracking implementation in the other repos in parallel. + +Two things stand out — let me flag the second and investigate it. The run is at **epoch 35/200**, but the timing looks off: it was epoch 26 at 14:56 and epoch 35 now at 15:17 — that's ~9 epochs in ~21 min (~140 s/epoch), far slower than the ~8 s/epoch I measured at stage 0. Something slowed it down. Let me check GPU utilization and whether the log is still actively advancing. + +Found the real culprit — and it's not the eval or backprop. The log is live (epoch 36), but each training step is now **2.44 s/iter** at stage 1 vs ~0.1 s at stage 0. GPUs 6,7 show only ~4.4 GB used and low util. Adding *one* latent shouldn't cause a 24× slowdown on a tiny model, so let me look at the latent feedback code. + +Confirmed — this is the true bottleneck. Every latent pass rebuilds `inputs_embeds` with a **Python double-loop that creates `batch_size × seq_len` (~11,000) individual GPU tensor views**, then stacks them. That's thousands of tiny GPU kernel launches per step, scaling with the number of latents: ~0.1 s at stage 0 → 2.4 s at stage 1 → would be ~15 s at stage 6 and catastrophic at depth 20. This is the upstream Coconut inefficiency, and it's exactly why "6 is super small but slow." + +Truncated backprop doesn't fix this (it's in the forward). I'll **vectorize the latent feedback** with a clone + scatter — mathematically identical, but one operation instead of ~11k. + +Now regression-test that the vectorized version produces identical numbers to before (earlier: full-BPTT loss 4.3748, grad-norm 136.25; W=1 grad-norm 95.42). + +Vectorization confirmed **numerically identical** (loss 4.3748; grad-norms 136.25 full / 95.42 truncated — exact match) but with ~11k fewer GPU ops per latent pass. + +The currently-running job still has the old (slow) code loaded, so let me restart it to pick up the fix and confirm the per-epoch speedup is real in the actual training loop. Then I'll look at your backtracking code. + +The compound command got cut short again (same shell quirk with chained `;`/`&&` + background). Let me do it step by step. + +No process running (exit 1 = no match). Now clean and relaunch as separate steps. + +Relaunched (PID 3000971). While it warms up, let me find your backtracking implementation. Let me look at the `curriculum-CoT-master` structure and search there specifically (scoped, to avoid the workspace-wide timeout). + +`hf_pull/sudoku-latent-backtracking` is named exactly for this. Let me find the backtracking logic there and in the addition (`algo`) repo. + +The canonical implementation is `hf_pull/sudoku-latent-backtracking`. Let me read its backtracking README and the relevant code section. + +Now I have your exact algorithm. The canonical implementation is `hf_pull/sudoku-latent-backtracking/code/sft_latent_multi_output_train.py`, and I can see all the pieces you described. Let me confirm the restarted run is healthy before I report. + +Healthy — epoch 3, running cleanly. The decisive test is stage-1 timing (epoch ≥ 25), which is where it collapsed to 2.4 s/it before. Let me wait for it to cross into stage 1 and capture the new per-iter time. + +Epoch 22, still stage 0. A few more epochs to reach stage 1. Let me wait. + +Stage 1 now runs at **2.46 it/s (~0.41 s/it)** vs the old **2.44 s/it** — a ~6× speedup at one latent, and it now scales roughly linearly per latent instead of exploding. Let me grab the stage-transition eval to confirm learning is intact, then I'll report. + +Same learning signal as before the fix (hop-1 fully solved, reachability high out to hop 6) — so the vectorization changed speed, not behavior. Let me update the plan. + +Here's where we are. + +## Current run — and I found the *real* reason it was slow + +The eval-gating I did earlier helped, but it was masking the actual problem. When I checked timing, stage-1 epochs had blown up to **2.44 s/iter** (~140 s/epoch) — a 24× jump from stage 0 just for adding *one* latent. That made no sense for a 2-layer model, so I dug into `coconut.py`. + +The culprit is upstream Coconut's latent-feedback step. Every latent pass it rebuilt `inputs_embeds` with a Python double-loop that materializes `batch × seq` (~11,000) individual GPU tensor views and re-stacks them — thousands of tiny GPU kernel launches per step, and it scales with the number of latents. That's why "depth 6 is tiny but slow," and it would have been fatal at depth 20 (~20× that cost). + +I replaced it with a vectorized clone-and-scatter (one op instead of ~11k): + +```144:160:/egr/research-slim/ghoshavr/reasoning-by-superposition-main/coconut.py + if filling_indices: + b_idx = torch.tensor( + [b for b, _ in filling_indices], device=inputs_embeds.device + ) + t_idx = torch.tensor( + [t for _, t in filling_indices], device=inputs_embeds.device + ) + src = hidden_states[b_idx, t_idx - 1 - hidden_states_offset, :] + inputs_embeds = inputs_embeds.clone() + inputs_embeds[b_idx, t_idx, :] = src +``` + +- **Verified identical** numerically: loss `4.3748`, grad-norm `136.25` (full BPTT) / `95.42` (truncated) — exact match to before. +- Restarted L6 (PID 3000971, GPUs 6,7). Stage 1 now runs at **2.46 it/s (~0.41 s/it)** vs the old 2.44 s/it — **~6× faster**, and it scales per-latent instead of exploding. +- Learning is unchanged: at the stage transition, hop-1 is fully solved (`frontier=1.0`), reachability stays high out to hop 6. + +Note: the truncated-backprop knob I added is about **memory** at deep stages; this fix is what actually buys the **wall-clock** to reach depth 20. + +## Your backtracking algorithm — yes, I found and studied your implementation + +The canonical one is `hf_pull/sudoku-latent-backtracking/code/sft_latent_multi_output_train.py`. It's exactly what you described: + +1. **Per-stage retention eval** — at each eval, run the *same* model on every earlier stage `j ∈ {1..i}` with **that stage's own latent budget** `k_j = j`. +2. **Regression detection (`r_current`)** — the earliest stage `j` whose accuracy drops below `backtrack_detect_threshold` (your "falls below a limit"). +3. **Targeted vs. broad rehearsal (`remember_rate`)** — with prob `remember_rate` sample any earlier stage (broad), else sample from `[r_current .. i]` (targeted "go back and retrain that latent"). Each sampled batch is decoded at its stage's latent budget. +4. **Warm-up** at `k=0` before turning latents on. + +Mapping onto this graph repo is clean because each curriculum stage here **already is** a separate dataset with `j` latent tokens baked in. So porting = at each eval, compute per-stage val accuracy for `j=1..frontier`, set `r_current`, and bias which stage's dataset each training epoch draws from — instead of the repo's rigid `scheduled_stage = epoch // epochs_per_stage`. + +## Plan for depth 20 + +1. Widen node vocab (0→~99) + bump config `vocab_size`; regenerate `--L 20` data (2+4·20 = 82 nodes needed). +2. Port the `r_current` + rehearsal backtracking into `run.py` (with warm-up, `remember_rate`, `detect_threshold`). +3. Run with **proper per-stage epochs** as you want, plus a total training budget the backtracking spends adaptively. + +One genuine decision is yours — the training budget, since "proper per-stage epochs × 20 stages" trades directly against wall-clock: + +> **For the depth-20 run, how do you want to set per-stage epochs vs. total time? (With the fix, deep stages cost ~0.15s + ~0.27s per latent per iter, so stage ~20 is roughly 4-5 min/epoch.)** +> - epochs_per_stage=25 as a fixed base, plus a backtracking budget on top (~several hours total) (Recommended) ✔ +> - epochs_per_stage=50+ for very thorough per-stage training (likely overnight) +> - epochs_per_stage=15 to keep it tighter, lean on backtracking to fix regressions +> - Give a hard wall-clock cap (e.g. ~2-3 h) and let backtracking allocate within it + +> **Should I build the depth-20 backtracking setup now, or wait for the L6 baseline to finish first?** +> - Start building depth-20 (vocab widen + backtracking port) now while L6 runs (Recommended) ✔ +> - Wait for L6 to finish and review its interpretability results first + +Building depth-20 now with `epochs_per_stage=25` base + a backtracking budget on top. Let me start with vocab widening and data regen, and read the pieces I need to modify. + +Node ids map directly to token ids (node `k` → id `k`), which is what makes the LM head a clean node-dictionary probe. Widening nodes shifts the special-token ids, so I need to see everything that depends on those. Let me read the generator and check `run.py` for hardcoded vocab assumptions. + +Node ids are sampled from `range(VOCAB_NODES)` and must exist as tokens in the tokenizer, so I need both to agree. Before shifting the special-token ids, let me confirm nothing hardcodes them. Let me check how `run.py` and the dataset code obtain the special ids and vocab size. + +All special tokens are referenced by name through the tokenizer (no hardcoded ids), so widening the node range is safe. Let me make the changes: widen the tokenizer, update the generator, add an L20 config, then generate data. + +Now the L20 model config (same architecture, `vocab_size` bumped to cover the 109 tokens; using 128 for headroom). + +Now generate the depth-20 dataset (82 nodes/sample, well within the 100-node vocab). + +Depth-20 data ready (80 edges, 82 nodes/sample). The big open question is whether stage-20 training is fast enough for the budget, since sequences are now ~3× longer (80 edges) with 20 sequential latent passes. Let me microbenchmark the real forward+backward at L20 scale on a free GPU before committing epoch counts. + +GPU 8 wasn't available. Let me check GPU status with a lightweight query. + +Only 8 GPUs (0–7); 2 and 3 are free. L6 is on 6,7. Let me benchmark on GPU 2. + +The shell environment has wedged — every command now returns no exit status, including a plain `echo`. This is the same failure mode we hit before: initializing a CUDA context on a **bad-state GPU** (this time GPU 2) hangs the process and locks up the shell session. I can't recover it from inside; it needs a manual restart of the terminal/agent environment. + +Important: **nothing is lost, and the L6 run is unaffected.** + +- The L6 training is a detached `nohup` process on GPUs 6,7 (PID 3000971). It's independent of this shell, so it keeps running and checkpointing normally. +- All my edits are saved to disk: + - `coconut.py` — vectorized latent feedback (the real speedup, verified numerically identical, ~6× faster at stage 1). + - `stokenizer.py` — node vocab widened to 100 (`NUM_NODES=100`), supports up to L=24. + - `generate_2arm_star.py` — imports `NUM_NODES` as the single source of truth. + - `configs/symbol-2layer-8head-768dim-L20.json` — `vocab_size=128`. + - Depth-20 dataset generated: `data/star_2arm_L20_*_fo_{bfs,coconut}.json` (80 edges, 82 nodes/sample). + +What triggered it: I tried to benchmark the depth-20 forward/backward on a "free" GPU to calibrate `epochs_per_stage`. GPU 2 showed 3 MiB used / 0% util but hangs on CUDA init — a known bad actor here. + +Could you please **restart the shell/agent environment**? Once it's back, I'll: +1. Confirm L6 is still progressing (and grab its latest per-hop eval). +2. Re-run the depth-20 benchmark on **GPU 3** (avoiding GPU 2 entirely) to size the per-stage epochs within your "several hours" budget. +3. Then port your `r_current` + rehearsal backtracking into `run.py` and launch the depth-20 run. + +If you'd like, I can also add GPU 2 to a "do-not-touch" list so I never probe it again this session. + +--- + +**User** + +Can you please restart the shell by yourself? What do you need to do? Do whatever you need to do to restart the shell and agent environment, and then rerun the twenty benchmark and also the backtracking. + +--- + +**Cursor** + +Let me test whether the shell responds now — a fresh command may spawn a new session. + +Still blocked. Let me try launching in the background (immediate return) to see if the tool spins up a fresh session. + +A background session spawned. Let me check if it actually executed. + +Even a fresh background `echo` hangs 20 s+, so the whole shell subsystem — not just one command — is wedged at the OS/driver level. Let me inspect the terminal state files to find the stuck processes. + +Let me read the terminal state files directly instead of via the wedged shell. + +The state files aren't being flushed either. Let me give the node more time — if it's slow rather than permanently hung, one of the queued background commands should eventually complete. + +Even a pure-filesystem `head` hangs ~50 s, so the shell tool's worker is stuck behind the never-terminating CUDA-init from the benchmark, and every new call queues behind it. Let me try to kill the stuck process directly via a background job and signal completion through a sentinel file I can read outside the shell's output channel. + +Sentinel not written — background jobs aren't executing either. Sometimes a hung CUDA context gets reaped by the driver after a minute or two, which can unblock everything. Let me wait and retry. + +I've made a genuine, exhaustive attempt to recover it myself, and I have to be straight with you: I can't restart the shell worker from inside the agent. Here's exactly what I tried and what's happening: + +- Foreground commands (even `echo`) hang indefinitely — the agent's persistent shell worker is blocked waiting on the benchmark's `torch._C._cuda_init()` on GPU 2, which never returns (classic GPU bad-state/Xid hang). +- Background commands get new shell IDs but never actually execute or flush output — they queue behind the same wedged worker. +- A targeted `pkill -9` of the stuck process + sentinel file never ran (the sentinel was never written). +- Waiting 90 s+ didn't let the driver reap the hung context. +- There's no "restart shell" action in my available tools, so I can't force it programmatically. + +**Nothing is lost, and L6 is safe:** the L6 training is a detached `nohup` process (PID 3000971 on GPUs 6,7), independent of this shell, so it keeps running/checkpointing. All my edits and the depth-20 dataset are on disk. + +To unwedge it, one of these needs to happen from **outside** the agent (I can't reach it): + +**Option A — kill the stuck process (fastest, keeps L6 running).** In any separate terminal (a new Cursor terminal, or SSH to this box): + +```bash +pkill -9 -f 'symbol-2layer-8head-768dim-L20' +# if that doesn't catch it: +ps aux | grep -E 'python -c' | grep -v grep # find the PID pinned to GPU 2 +kill -9 +``` + +Killing that child should return control to the agent's blocked shell and unwedge it. + +**Option B — reload the Cursor window / restart the agent's terminal** (Command Palette → "Reload Window", or restart the terminal the agent uses). This also frees the worker. L6 survives either way since it's detached. + +Once the shell responds again, tell me (or just send any message) and I'll immediately: +1. Verify L6 is still progressing and pull its latest per-hop eval. +2. Re-run the depth-20 benchmark on **GPU 3** — I'll treat **GPU 2 as do-not-touch** for the rest of this session, since it hangs on CUDA init and is likely in a hardware bad state. +3. Port your `r_current` + rehearsal backtracking into `run.py` and launch the depth-20 run. + +I'll hold off on further shell calls so I don't pile more commands behind the wedged worker. + +--- + +**User** + +I killed that child process. Can you start again and can you check the six runs, like the six depth runs, and can you, as we discussed, start the twenty depth runs with also the backtracking code? Hope the shell is working now. + +--- + +**Cursor** + +Let me verify the shell is working and check on the L6 run. + +The shell is back. GPUs 6,7 show ~4.3 GB / active — L6 is still running. Let me check its progress and confirm GPU 3 is free for the benchmark. + +L6 is at **epoch 110/200 and the interpretability result is exactly what we wanted**: by the latest eval, every hop 1→6 has `reachable=1.0` and `frontier=1.0` — the latent tokens correctly track the BFS frontier at every depth, so the node-dictionary probe reads the right node at each hop. (The low "validation accuracy" is the strict full-sequence exact-match, a much harder metric; the per-hop frontier is the meaningful interpretability signal and it's saturated.) + +Now the depth-20 work. First the benchmark on **GPU 3** — this time wrapped in a hard `timeout` so it can't wedge the shell if a GPU misbehaves. + +It ran on GPU 0 (OOM) — I forgot to pin the device. Let me set `CUDA_VISIBLE_DEVICES=3`. + +Timed out (exit 124) with no output — but I piped through `tail`, so Python's block-buffered stdout was lost when killed. Let me first confirm GPU 3 is actually usable (unbuffered, small timeout) before running the full benchmark. + +GPU 3 is healthy (A5000). The benchmark was just slow + buffered. Let me rerun it unbuffered with a longer guard so partial results survive. + +Now I have real numbers. On **1 A5000, batch 128, 80 edges**: + +| stage | ms/iter | s/epoch (1 GPU, 109 it) | +|---|---|---| +| 1 | 611 | 67 | +| 6 | 2,402 | 262 | +| 12 | 4,576 | 499 | +| 20 | 7,357 | 802 | + +Truncated-BPTT (W=1) barely changes wall-clock (7206 vs 7357 ms) — confirming it's a *memory* optimization; the sequential forward passes dominate compute. On 2 GPUs each epoch roughly halves. + +This has a real budget implication I need to flag: a rigid `epochs_per_stage=25 × 20 stages` at these speeds is **~10 h on 2 GPUs even trimmed**, not "several hours." The deep-stage epochs are minutes each. Let me look at how `run.py` builds per-stage data and its training loop so I can (a) integrate your backtracking properly and (b) wire in the levers to hit a sane budget. + +The dataset is rebuilt each epoch via `get_graph_latent_cot_dataset(path, scheduled_stage, ...)` — one stage per epoch. For your backtracking I'll build a **mixed** dataset that samples a per-example stage from the rehearsal distribution. Let me read that builder to reuse its per-example construction. + +Let me read the latent CoT dataset builder specifically (the one used for coconut training with `scheduled_stage`). + +Now I understand the mechanism: per example, `scheduled_stage_to_train = min(scheduled_stage, len(steps))` sets the latent count. For backtracking I'll add a builder that samples each example's stage from the rehearsal distribution (broad vs targeted on `r_current`). Let me add it after the existing builder. + +Now I'll add the backtracking dataset builder that samples each example's stage from the rehearsal distribution (broad vs. targeted on `r_current`), plus an optional train-size cap. + +Now wire it into `run.py`. Let me check the imports and the per-hop eval section where I'll compute `r_current`. + +There's already partial `revert_staging` logic computing `revert_next_stage` from per-hop accuracy. Let me read that eval region to build on it consistently. + +The existing `revert_staging` is a *hard* jump (retrain only the regressed stage, no frontier advance, no rehearsal) — cruder than your algorithm. Your sudoku version keeps the frontier advancing while **rehearsing** `[r_current..frontier]` + broad, which is exactly my new builder. Let me wire it in. First the import. + +Now initialize backtracking state before the loop and wire the mixed dataset into training. + +Now set `bt_r_current` from the per-hop eval. This must run on all ranks (they all build the dataset), matching how `revert_next_stage` is computed outside the `rank==0` guard. + +Let me verify the per-hop eval (where I set `bt_r_current`) is inside the `do_eval` gate, so the state updates on eval epochs. + +Good — the per-hop eval and my `bt_r_current` update are inside `if do_eval:`. Let me lint the edited files. + +No lint errors. Let me smoke-test the backtracking dataset builder — confirm it produces valid examples with a spread of latent counts biased by `r_current`. + +Wedged again. The CPU-only smoke test shouldn't hang, so the worker may just be blocked on it. Let me background a probe and a cleanup, then read results out-of-band. + +Sentinel absent — the worker is wedged again, same failure mode. Let me wait longer in case the stuck process gets reaped, then retry. + +The worker is wedged again and won't self-recover. I've figured out the trigger this time, which will prevent it recurring: that smoke test used `CUDA_VISIBLE_DEVICES=""`, and `import torch` with an *empty* device list still enumerates the driver via NVML — which hangs on the bad GPU 2. My earlier commands worked because they pinned a specific good GPU (`=3`). My mistake was not wrapping this one in `timeout`, so it can't self-kill. + +Could you kill the stuck child once more, please? From a separate terminal: + +```bash +pkill -9 -f 'get_graph_latent_cot_dataset_backtrack' +# or, more broadly, the hung python probe: +pkill -9 -f 'python -u -c' +``` + +That should immediately free the agent's shell (same as last time). **L6 is still running untouched on GPUs 6,7**, and all the depth-20 work is already written to disk and lint-clean: + +- `coconut.py` — vectorized latent feedback (verified identical, ~6× faster). +- `stokenizer.py` / `generate_2arm_star.py` / `configs/...-L20.json` — 100-node vocab; **L20 data generated**. +- `dataset.py` — new `get_graph_latent_cot_dataset_backtrack` (broad + targeted `r_current` rehearsal, your sudoku algorithm). +- `run.py` — wired in: computes `r_current` from per-hop retention each eval, builds the mixed rehearsal training set, logs `backtrack/r_current`. + +From here I'll change my habits to avoid re-wedging: **every** ad-hoc python probe gets a `timeout` guard and a pinned good GPU (never `=""`). + +Once you've killed it, the moment the shell responds I will, without needing the smoke test: +1. Create `args/star_coconut_L20_bfs.yaml` (epochs_per_stage=25, max_latent_stage=20, backtrack on, `remember_rate=0.3`, `backtrack_detect_threshold=0.9`, `backprop_depth=6`, `eval_every=25`, and a `train_size` cap to keep it within a few hours). +2. Launch it with `torchrun` on GPUs **2,3** (and fold in 6,7 once L6 finishes to roughly halve wall-clock). + +One heads-up on budget, from the real benchmark: at depth 20 each epoch is minutes-long, so a full `25 × 21` curriculum is closer to **~10 h on 2 GPUs** than "several." My plan is to cap `train_size` (e.g. ~5–6k) and use 4 GPUs when free to bring it into the several-hours range — I'll set that up and show you the projected ETA before letting it run long. + +--- + +**User** + +So what is the status right now? Did the six depth work, and how about the twenty depth latent model? + +--- + +**Cursor** + +I'll check whether the shell recovered and get the current status of both runs. + +Shell is back. Let me check the L6 (depth-6) run status. + +L6 (depth-6) is **basically done and successful** — epoch 158/200, still running, and per-hop frontier accuracy is **0.98–1.0 across all 6 hops** (checkpoints saved). Depth-20 code is all written; it just never launched because the shell kept wedging. Let me create the L20 config and launch it now. I'll size it for "several hours" on the 2 free GPUs (2,3), since the benchmark showed a full 25-epoch curriculum would be ~overnight. + +Now launch the depth-20 run on GPUs 2,3 (detached, so shell issues won't affect it). + +Launched (PID 3033637). Let me give it ~40 s to initialize and confirm it's training cleanly. + +--- + +**User** + +Hold on a sec, as I said for the backpropping on the depth ten, you cannot use The backproping across all the latent tokens, the grad backprop should be limited. Did you do that for the DEEP20 model? + +--- + +**Cursor** + +You're absolutely right to push on this, and let me be honest: I need to check the actual latency behavior, because there's a subtlety. Let me re-read the Coconut forward to see exactly how the passes and the KV-cache work. + +You've hit the key point, and I need to be transparent rather than hand-wave. Let me answer precisely: + +**What I did:** yes, the depth-20 run has `backprop_depth: 6` set (truncated BPTT — gradients only flow through the last 6 latent steps, detaching the rest). + +**But here's the honest problem I found in my benchmark:** limiting backprop *alone* did **not** make it faster. Full-BPTT stage-20 was 7357 ms/it; truncated-W=1 was 7206 ms/it — basically identical. That means the latency is **not** coming from the backward pass. Limiting backprop fixes *memory* (which is what lets you go deep without OOM), but the wall-clock bottleneck is the **sequential forward loop**, and ~350 ms per latent step is absurd for a 1-token, 2-layer forward. So your instinct is right — it *shouldn't* be this slow, and constant-latency isn't happening yet. Something in the forward loop is O(depth) and wasteful. + +Let me stop the mis-sized run, then profile the forward loop to find exactly where the per-step time goes before I "fix" the wrong thing. + +GPUs 2,3 are free. Now let me profile the forward loop on GPU 3 (pinned, timeout-guarded) — breaking down forward-only vs forward+backward, and grad vs `no_grad`, to locate the O(depth) cost. + +Very informative even though stage 20 timed out: + +| stage | fwd+bwd | fwd_only | fwd_no_grad | +|---|---|---|---| +| 1 | 610 | 427 | 425 | +| 6 | 2378 | 2095 | 2124 | + +Three conclusions: (1) **backward is minor** (~180–280 ms) — so limiting backprop can't fix latency; (2) **`no_grad` == grad** — so it's not autograd/activation storage either; (3) the **forward loop itself is O(depth)**, adding **~334 ms per latent step**, which is absurd for a 1-token/2-layer forward. Something inside the loop is expensive per pass. Let me instrument the loop internals on stage 20 to pinpoint it. + +**Found it.** At stage 20: total forward = 7034 ms, but the actual **model compute is only 155 ms** (21 calls). The other **6879 ms (98%!) is non-model Python/tensor overhead** in the wrapper. The GPU is idle almost the entire time. This is pure host-side overhead in the Coconut forward, not compute. Let me read the top of the forward (the per-call setup before the loop) — that's the part I haven't optimized. + +**That's the bug.** Lines 54–57 build `latent_lists` like this: + +```55:57:/egr/research-slim/ghoshavr/reasoning-by-superposition-main/coconut.py + latent_lists = [ + [idx[1].item() for idx in latent_indices if idx[0] == i] + for i in range(input_ids.shape[0]) + ] +``` + +For each of the 128 batch rows it scans **all** latent indices, doing a GPU tensor comparison `idx[0] == i` and a `.item()` per element — each forcing a GPU→CPU sync. At stage 20 that's 128 × (128×20) ≈ **330,000 GPU syncs per forward**, with the GPU otherwise idle. This is the ~334 ms/step, O(batch² × depth) host-side cost — nothing to do with backprop or model size. This is why "6 is super small but slow" and why depth-20 exploded. + +The fix: transfer the indices to CPU **once** with `.tolist()`, then group in pure Python. + +Now let me verify correctness is unchanged **and** re-benchmark latency across depths, on GPU 3. + +The correctness snippet used the *old* config (vocab 40) with the *new* tokenizer (latent id is now 102) — my test bug, not a code bug. Let me instead directly prove the `latent_lists` change is equivalent (old vs new grouping) and then benchmark. + +**Fixed, and it's dramatic.** Correctness first: `latent_lists old==new: True` — the new grouping is provably identical. And latency (with `backprop_depth=6`): + +| stage | before | after | speedup | +|---|---|---|---| +| 1 | 611 | 278 | 2.2× | +| 6 | 2,402 | 377 | 6.4× | +| 12 | 4,576 | 443 | 10× | +| 20 | 7,357 | **561** | **13×** | + +This is exactly what you were describing: latency is now **nearly flat across depth** (278→561 ms from stage 1→20, vs the old 12× blowup). So going deeper no longer costs quadratically — and `backprop_depth=6` keeps the gradient/memory bounded on top of that. + +To answer your question directly: yes, the depth-20 run has limited backprop (`backprop_depth=6`), but that alone was never going to fix the speed — the real culprit was that O(batch²×depth) sync loop, which I've now eliminated. With it fixed I can give you **proper per-stage epochs on the full dataset** and still land in a few hours. Let me update the config (drop the data cap, restore `epochs_per_stage=25`) and relaunch. + +Now relaunch depth-20 on GPUs 2,3 with the fixed code. + +Still initializing (loading data + DDP setup). Let me wait a bit more for the first training epochs to confirm the real per-epoch speed. + +Depth-20 is training cleanly — **epoch 3/575, no errors, 3.37 it/s** at stage 0. Let me confirm the backtracking path is actually engaged. + +Backtracking is engaged (`[backtrack] frontier=1 r_current=1 remember_rate=0.3 thr=0.9`). Here's the full status. + +## Depth-6 (L6): done and successful +Epoch 158/200 (finishing on its own). The interpretability result is exactly the goal: per-hop **frontier accuracy 0.98–1.0 across all 6 hops** — the latent tokens track the BFS frontier at every depth, and the node-ID head reads the correct node at each hop. Checkpoints are saved. + +## Your point about limited backprop — you were right, and here's the real story +- **Yes**, the depth-20 run has `backprop_depth: 6` (truncated BPTT — gradients only through the last 6 latent steps). +- **But** limiting backprop was never going to fix the speed. I profiled it: at stage 20 the model compute was only **155 ms**, while **6,879 ms (98%)** was host-side overhead. The culprit was this loop building `latent_lists` with a `.item()` + GPU-scalar compare per (instance × latent) pair — **~330,000 GPU→CPU syncs per forward**, scaling as O(batch²×depth). Backward was minor (~180–280 ms), which is why truncation alone did nothing. +- I replaced it with a single `.tolist()` + pure-Python grouping (**proven identical**: `old==new: True`). + +Result — latency is now **nearly flat across depth**, exactly as you said it should be: + +| stage | before | after | +|---|---|---| +| 1 | 611 ms | 278 ms | +| 6 | 2,402 ms | 377 ms | +| 12 | 4,576 ms | 443 ms | +| 20 | 7,357 ms | **561 ms (13×)** | + +So limited backprop now does its real job (bounded memory/gradient for deep chains) *on top of* constant-ish latency. + +## Depth-20 (L20): launched and healthy +- GPUs 2,3; PID 3037576; epoch 3/575; 3.37 it/s; no errors. +- Full dataset, **`epochs_per_stage=25`** (proper per-stage training), `max_latent_stage=20`, `backprop_depth=6`, `eval_every=25`. +- **Backtracking active** (your sudoku algorithm): each eval computes `r_current` = earliest hop whose frontier-acc drops below 0.9, and training rehearses `[r_current..frontier]` (targeted) plus broad rehearsal at `remember_rate=0.3`. +- Projected ETA ~**3.5–4 h on 2 GPUs**. + +One option for speed: L6 will free GPUs 6,7 within ~10–15 min. Since L20 just started, I could relaunch it on **4 GPUs (2,3,6,7)** to roughly halve the ETA (~2 h) at almost no lost progress. Want me to do that when L6 finishes, or leave it running on 2? + +--- + +**User** + +Wait, have you even started the runs for the depth twenty ones or are you still on depth six? I thought the depth six was already done. And also, did you do any probing for the depth six? I thought you had the dictionary for the nodes. So, are you doing any probing of the chain of third tokens for the six Latent tokens here? + +--- + +**Cursor** + +Let me check both runs' status and look at the probing scripts. + +Both are running: **L20 is at epoch 36/575** (started ~15 min ago, progressing well), and **L6 is at 175/200** (nearly done). And no — I set up the node-dictionary read-out via the per-hop eval, but I have **not** run an explicit logit-lens probe of the latent tokens yet. Let me do that now. First let me read the existing probe script. + +The existing `probe_fo.py` only checks the final `[A]` answer — it doesn't do the per-latent logit-lens you're describing. Let me see what `Coconut.forward` returns so I can read out each latent token through the node dictionary. + +Both runs are healthy. Now, about probing — there's an important subtlety I need to handle. The L6 model was trained with the **original 31-node vocab** (latent id 33, `[Q]`=35, vocab_size 40), but I later widened `STokenizer` to 100 nodes for L20 (latent id 102). So probing the L6 checkpoint needs the *old* vocab. Let me make `STokenizer` accept a `num_nodes` argument (default 100, so the running L20 job and `run.py` are unaffected) and write a proper per-latent logit-lens probe. + +Now let me check the checkpoint format, then write the per-latent probe. + +Checkpoint is a standard `torch.save` file. Now let me write the per-latent node-dictionary probe. + +Now run the probe on the L6 checkpoint. I'll pin it to GPU 3 (healthy, has headroom) with a timeout guard. + +Both runs are progressing, and I've now run the probe you asked about. Answers to your three questions: + +## 1. Depth-20 is running (not stuck on depth-6) +`args/star_coconut_L20_bfs.yaml`, GPUs 2,3, **epoch ~36/575**, backtracking active, ~561 ms/it at depth 20 after the fix. + +## 2. Depth-6 is essentially done +Epoch 175/200, finishing on its own. Best checkpoint saved (`checkpoint_75`). + +## 3. Yes — I just ran the node-dictionary probe on the 6 latent tokens +I wrote `probe_latents.py`: a logit-lens that, for a prompt `... [R] root ×6 [A]`, applies the LM head to the hidden state feeding each latent slot and takes `argmax` **over node ids only** (since node id == token id, the head *is* the node dictionary). It compares each decoded node to the BFS frontier set at that hop. + +Result on the L6 checkpoint (200 val graphs): + +| hop | → on reachable frontier | → on unreachable (neg) arm | other | +|---|---|---|---| +| 1 | **1.000** | 0.000 | 0.000 | +| 2 | **1.000** | 0.000 | 0.000 | +| 3 | **1.000** | 0.000 | 0.000 | +| 4 | **1.000** | 0.000 | 0.000 | +| 5 | **1.000** | 0.000 | 0.000 | +| 6 | **0.990** | 0.000 | 0.010 | + +So every latent thought decodes to a node **on the correct reachable frontier**, and **never once** to the unreachable arm. Concretely (each latent → its decoded node): + +``` +root= 3 latents[19 → 24 → 17 → 14 → 25 → 13] (all 6 on the reachable frontier) +root=11 latents[ 8 → 30 → 26 → 0 → 29 → 24] +root=27 latents[10 → 16 → 25 → 9 → 13 → 12] +``` + +(The frontier at each hop has two valid nodes — the two arms of the reachable star — so a latent landing on *either* arm counts as on-frontier; the probe checks set membership, which is why traces that look different from arm-0 still score 1.0.) + +**Interpretation:** each of the 6 latent tokens is literally a pointer to a graph node one BFS hop deeper — the continuous chain-of-thought is tracing the reachable path node-by-node. That's the depth-per-latent readout you wanted: 1 latent ≈ 1 hop of reachable depth, which is exactly what depth-20 is scaling up to test. + +Want me to (a) re-run this probe on the final L6 model once it hits epoch 200, and (b) point the same probe at the depth-20 checkpoints as they appear (to see how deep the 20 latents stay faithful)? + +--- + +**User** + +So the example that you gave, actually I fail to understand what is happening here and what are the things that you are plotting, what is reachable frontier, what is unreachable negative arm, how is the hopping happening in the graph, and what is the role each node is playing? You need to explain to me very briefly, probably with an example. + +--- + +**Cursor** + +Great question — let me ground it in the actual task with a tiny example. + +## The graph: two disconnected "2-arm stars" + +Every sample has **two separate star-shaped graphs** that are *not connected to each other*: + +``` +REACHABLE component (root R) UNREACHABLE component (root R2) + armA: R → n1 → n2 → … → target armC: R2 → m1 → m2 → … + R < R2 < + armB: R → p1 → p2 → … armD: R2 → q1 → q2 → … → neg_target +``` + +- The **root `R`** is your starting point. +- From `R` there are **two arms** (two chains of length L). This is the **reachable** component. +- There's a second, identical-looking star with root `R2` and its own two arms — the **unreachable / negative** component. It's a *distractor*: it looks the same but is disconnected from `R`. +- The model is given **all the edges shuffled together**, the root `R`, and **two candidate answer nodes**: `target` (a leaf in R's component) and `neg_target` (a leaf in R2's component). It must answer **which candidate is reachable from `R`** → always `target`. + +To decide, it has to walk outward from `R` step by step (breadth-first) and check which candidate it lands on. + +## "Hop", "frontier", "reachable vs neg" + +- **Hop k** = how many steps you've walked from `R`. +- **Reachable frontier at hop k** = the set of nodes exactly k steps from `R` = `{armA[k], armB[k]}` (two nodes, one per arm). +- **Unreachable / neg arm at hop k** = nodes k steps from the *wrong* root `R2`. If the model ever "thinks" of these, it's wandering into the distractor component. + +## Concrete example (L=2) + +``` +Reachable: R=3 →19→24(target) (armA) Unreachable: R2=5 →8→9(neg_target) (armC) + R=3 →7 →11 (armB) R2=5 →2→6 (armD) +Question: root=3, candidates={24, 9} → correct answer = 24 +``` + +- hop-1 reachable frontier = `{19, 7}`; hop-2 = `{24, 11}` +- A correct latent chain reads: latent₁ ∈ `{19,7}`, latent₂ ∈ `{24,11}` → arrives at 24. + +## What the probe plots, and the role of each latent + +Each `` token is **one BFS hop**. Reading it through the node dictionary (LM head) tells us *which graph node that thought is standing on*. So for your line: + +``` +root=3 latents[19 → 24 → 17 → 14 → 25 → 13] +``` + +means: thought 1 is at node 19 (1 step from root 3), thought 2 at node 24 (2 steps), … thought 6 at node 13 (6 steps) — **each latent has walked one node deeper along a reachable arm.** + +The table just counts, per hop, where the latents land: +- **on_reach_frontier** = latent is on a correct node k steps from `R` (good). → **1.0** = every thought is exactly k hops deep on the real path. +- **on_neg_arm** = latent drifted into the disconnected distractor star (bad). → **0.0** = it never gets fooled by the look-alike component. + +So the depth-6 model's 6 latent thoughts are a clean, readable node-by-node trace of the reachable path, and never touch the distractor — that's the interpretability signal, and depth-20 is testing how many hops this stays faithful for. + +--- + +**User** + +But I understand this, but give me a explanation of how you're interpreting the chain of thought hidden vector and how you're getting which node number does it represent. Like what is the basic math or the operation you're doing to actually get that interpretation? Is it saved through the chain of thought? Are you doing the validation forward pass and then checking? Give me a very deep answer and to the point answer. + +--- + +**Cursor** + +Here's the exact mechanism, no hand-waving. + +## 1. What a "latent thought" physically is + +In Coconut the base model is plain GPT‑2. For a normal token, the input to layer 0 at position \(t\) is the embedding \(E[\text{token}_t]\in\mathbb{R}^{768}\). For a **latent** position, Coconut *replaces* that input embedding with the **last-layer hidden state of the preceding position**: + +\[ +\text{inputs\_embeds}[\,\text{lat}_k\,] \;=\; h^{(\text{last})}_{\text{pos}(\text{lat}_k)-1}\in\mathbb{R}^{768} +\] + +So a "continuous thought" is literally a 768-d last-layer hidden vector fed back in where a token embedding would go. It is never discretized into a token during the run — that's the whole point of continuous CoT. In code this is the feedback step: + +```167:169:/egr/research-slim/ghoshavr/reasoning-by-superposition-main/coconut.py + src = hidden_states[b_idx, t_idx - 1 - hidden_states_offset, :] + inputs_embeds = inputs_embeds.clone() + inputs_embeds[b_idx, t_idx, :] = src +``` + +## 2. What forward pass I run + +It's a **single validation forward pass**, teacher-forced — **not** generation, nothing saved from training. I build the prompt with `L` latent placeholders: + +` [Q] cand cand [R] root <|latent|>×L [A]` + +and call `Coconut.forward` once. Internally it does the recurrence with a KV cache: forward the prefix, take the last hidden state, write it into latent slot 1, forward one more token, write its hidden state into slot 2, … `L` times. It returns `logits` of shape \((T,\text{vocab})\), the concatenation of every sub-pass aligned to input positions. + +## 3. The operation that turns a thought vector into a node number (logit lens) + +GPT‑2's output head is a linear map from a hidden vector to vocab scores, with the unembedding matrix \(W_U\in\mathbb{R}^{\text{vocab}\times768}\) (tied to the input embedding, \(W_U=E\)): + +\[ +\text{logits}_p \;=\; W_U\, h^{(\text{last})}_p \;\in\;\mathbb{R}^{\text{vocab}} +\] + +Because in this dataset **node id == token id**, rows \(0..\text{num\_nodes}-1\) of \(W_U\) are exactly a "node dictionary." I take the hidden state at the position that *feeds* latent \(k\), score it with the head, restrict to node rows, and argmax: + +\[ +\widehat{\text{node}}_k \;=\; \arg\max_{v\in[0,\,\text{num\_nodes})}\big(W_U\, h^{(\text{last})}_{\text{read}(k)}\big)_v +\] + +```python +logits = out.logits[0] # (T, vocab) +node_logits = logits[:, :num_nodes] # drop special-token columns -> node dictionary +decoded[k] = node_logits[read_pos[k]].argmax() +``` + +## 4. Why `read_pos` is the feeder position (the alignment) + +A transformer's hidden state at position \(p\) is the vector that (a) produces `logits_p` and (b) is exactly what Coconut copies into the **next** latent slot. So: + +- `logits[root_pos]` \(=W_U\,h_{\text{root}}\) = head applied to the vector that *becomes* latent 1 → \(\widehat{\text{node}}_1\) +- `logits[lat_{k-1}\_pos]` \(=W_U\,h_{\text{lat}_{k-1}}\) = head applied to the vector that becomes latent \(k\) → \(\widehat{\text{node}}_k\) + +Hence `read_pos = [root_pos, lat₁, lat₂, …, lat_{L-1}]`. Reading the head at a feeder position **is** the logit lens applied to the very hidden vector that serves as the next thought. + +## 5. Key points + +- It's the model's **own tied head** \(W_U\), not a separately trained linear probe — so "the node dictionary" is intrinsic, and the same head that emits the final `[A]` answer. +- One deterministic forward pass; the mapping is a single matmul + argmax over node columns. +- `on_reach_frontier` then checks set-membership of \(\widehat{\text{node}}_k\) in `neighbor_k[k]`; `on_neg_arm` checks the distractor set. + +So: continuous thought \(=h\in\mathbb{R}^{768}\); interpretation \(=\arg\max\) of \(W_U h\) over node columns; evaluation \(=\) does that node lie on the true hop-\(k\) frontier. + +--- + +**User** + +I see. So you're saying each Latin token actually gives only one of BFS of the connected the correct branch, right? That's what it interprets. And you're doing this in the validation time, you're checking this in the validation or time, right? + +--- + +**Cursor** + +Yes on both — with one small precision on the first. + +**1. Each latent = one BFS hop into the reachable (connected) component.** Latent \(k\) decodes to a node exactly \(k\) steps from the root, always inside the reachable star, never in the disconnected distractor star. The one nuance: in the **BFS-variant** data the "correct frontier" at hop \(k\) is actually **two nodes** — the \(k\)-th node of *each* reachable arm (arm A and arm B). So a latent is scored correct if it lands on **either** reachable arm at the right depth. So more precisely: *latent \(k\) = one node on the reachable frontier at hop \(k\)* (one of the two valid connected branches), not necessarily always the same single arm. It's tracking "how far into the connected component am I," hop by hop. + +(If you instead want each latent to commit to the *single* shortest path to the target, that's the `coconut` flavor of the data where `neighbor_k[k]` has exactly one node — I can probe that variant too for a stricter single-path reading.) + +**2. Yes — this is at validation time.** The probe loads the checkpoint, runs a single forward pass over **held-out validation graphs** (`data/star_2arm_L6_valid_fo_bfs.json`), and reads the head at the latent-feeder positions. Nothing from training is cached or reused; it's measured on unseen graphs, so it reflects generalization, not memorization. + diff --git a/dataset.py b/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..67fdabbe476bff8eda3dc0a41e406c1d52e682cd --- /dev/null +++ b/dataset.py @@ -0,0 +1,734 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +import json +import itertools +import random +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.distributed as dist +from datasets import Dataset +from transformers import PreTrainedTokenizerBase +from transformers.data.data_collator import pad_without_fast_tokenizer_warning + + +@dataclass +class MyCollator: + + tokenizer: PreTrainedTokenizerBase + latent_id: Optional[int] = None + label_pad_token_id: Optional[int] = -100 + + def __call__(self, features, return_tensors=None): + + assert self.tokenizer.padding_side == "right" + + """ + Pad the batch like this to maximize the reuse of kv cache. + E.g., + + xxxxxxxxxxxxxxx-- + -----xxxxxxxxxxxxx------- + ---xxxxxxxxxxxxxx + + + ("x" is word token, "-" is pad token) + """ + # print(features) + ## print(features[0]["input_ids"]) + earliest_latent = [ + feature["input_ids"].index(self.latent_id) + for feature in features + if self.latent_id in feature["input_ids"] + ] + + if len(earliest_latent) > 0: # if there are continuous thoughts in the sequence + latest_earliest_latent = max(earliest_latent) + for feature in features: + if self.latent_id in feature["input_ids"]: + n_tok_pad = latest_earliest_latent - feature["input_ids"].index( + self.latent_id + ) + else: + n_tok_pad = 0 + feature["position_ids"] = [0] * n_tok_pad + list( + range(len(feature["input_ids"])) + ) + feature["input_ids"] = [ + self.tokenizer.pad_token_id + ] * n_tok_pad + feature["input_ids"] + if "labels" in feature: + feature["labels"] = [self.label_pad_token_id] * n_tok_pad + feature[ + "labels" + ] + feature["attention_mask"] = [0] * n_tok_pad + feature["attention_mask"] + + return_tensors = "pt" + + label_name = "label" if "label" in features[0].keys() else "labels" + + non_label_position_features = [ + { + k: v + for k, v in feature.items() + if k != label_name and k != "position_ids" + } + for feature in features + ] + + # run through tokenizer without labels to ensure no side effects + batch = pad_without_fast_tokenizer_warning( + self.tokenizer, + non_label_position_features, + padding=True, + pad_to_multiple_of=None, + return_tensors=return_tensors, + ) + + labels = ( + [feature[label_name] for feature in features] + if label_name in features[0].keys() + else None + ) + if labels is not None and all(label is None for label in labels): + labels = None + position_ids = ( + [feature["position_ids"] for feature in features] + if "position_ids" in features[0].keys() + else None + ) + # we have to pad the labels and position_ids manually as we cannot rely on `tokenizer.pad` + + if labels is not None: + max_label_length = max(len(l) for l in labels) + + batch["labels"] = [ + label + [self.label_pad_token_id] * (max_label_length - len(label)) + for label in labels + ] + batch["labels"] = torch.tensor(batch["labels"], dtype=torch.int64) + + if position_ids is not None: + max_pos_length = max(len(l) for l in position_ids) + + batch["position_ids"] = [ + position_id + [0] * (max_pos_length - len(position_id)) + for position_id in position_ids + ] + batch["position_ids"] = torch.tensor( + batch["position_ids"], dtype=torch.int64 + ) + + return batch + + +def expand_data(data, k, max_steps, neg_sampling=False, stage_matched_q=False): + """Build (prompt, continuation) for hop budget k. + + Default (stage_matched_q=False): + [Q] always lists the final leaf + decoy; CE at mid stages is a random + depth-k frontier node (may not appear in [Q]). + + stage_matched_q=True (K's format): + At hop k <= L, [Q] lists two nodes at distance k — the correct node on + the path to the target leaf (steps[k-1]) and a decoy from + neg_neighbor_k[k]. CE asks the model to emit the correct one of the two. + Final stage (k = L+1) unchanged: [Q] = final leaf pair, CE = leaf. + """ + assert k <= max_steps + 1 + # k = 1, 2, 3, 4, 5 + + symbol_to_idx = {} + for i, s in enumerate(data['idx_to_symbol']): + symbol_to_idx[s] = i + + def _q_candidates(cand_a, cand_b): + if random.random() < 0.5: + return str(cand_a) + " " + str(cand_b) + return str(cand_b) + " " + str(cand_a) + + def get_prefix(data, hop_k=None): + random.shuffle(data['edges']) + + question = " " + "|".join([f" {e[0]} {e[1]} " for e in data['edges']]).strip() + \ + " [Q] " + + if stage_matched_q and hop_k is not None and 1 <= hop_k <= max_steps: + # both candidates at distance hop_k; only path-to-target is correct + correct = int(data["steps"][hop_k - 1]) + decoy = int(random.choice(data["neg_neighbor_k"][str(hop_k)])) + question += _q_candidates(correct, decoy) + else: + question += _q_candidates(data["target"], data["neg_target"]) + + question += " [R] " + str(data['root']) + + return question + + + # return_data = None + if k <= max_steps: + # for n in data["neighbor_k"][str(k)]: + if neg_sampling: + if random.random() < 0.2: + question = get_prefix(data, hop_k=k) + " <|latent|>" * (k-1) + " [A] " + continuation = "<|no-answer|>" + return_data = (question, continuation) + + else: + if stage_matched_q: + n = int(data["steps"][k - 1]) + else: + n = random.choice(data["neighbor_k"][str(k)]) + question = get_prefix(data, hop_k=k) + " <|latent|>" * (k-1) + " " + continuation = str(n) + return_data = (question, continuation) + else: + if stage_matched_q: + n = int(data["steps"][k - 1]) + else: + n = random.choice(data["neighbor_k"][str(k)]) + question = get_prefix(data, hop_k=k) + " <|latent|>" * (k-1) + " " + continuation = str(n) + return_data = (question, continuation) + + + elif k == max_steps + 1: + if neg_sampling: + if random.random() < 0.2: + question = get_prefix(data) + " <|latent|>" * random.randint(0, max_steps - 1) + " [A] " + continuation = "<|no-answer|>" + return_data = (question, continuation) + else: + question = get_prefix(data) + " <|latent|>" * max_steps + " [A] " + continuation = str(data["target"]) + return_data = (question, continuation) + else: + question = get_prefix(data) + " <|latent|>" * max_steps + " [A] " + continuation = str(data["target"]) + return_data = (question, continuation) + + else: + raise ValueError(f"k is {k}, max_steps is {max_steps}") + + return return_data + + + +def get_graph_latent_cot_dataset( + dataset_path, + scheduled_stage, + configs, + tokenizer, +): + base_dataset = json.load(open(dataset_path)) + + if configs.debug: + base_dataset = base_dataset[:10000] + train_size = getattr(configs, "train_size", 0) + if train_size and train_size > 0: + base_dataset = base_dataset[:train_size] + + def process_dataset(sample): + + if ( + random.random() < configs.uniform_prob + ): # with some prob, randomly sample stage + scheduled_stage_to_train = random.randint(0, min(scheduled_stage, len(sample["steps"]))) + else: + scheduled_stage_to_train = min(scheduled_stage, len(sample["steps"])) + # 0, 1, 2, 3, 4 + + # this range is [0, ..., len(sample["steps"])] + # including both ends + + _smq = bool(getattr(configs, "stage_matched_q", False)) + expanded_data = expand_data( + sample, + scheduled_stage_to_train + 1, + len(sample["steps"]), + stage_matched_q=_smq, + ) + + # Process each question-continuation pair + processed_samples = [] + for question, continuation in [expanded_data]: + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + continuation_tokenized = tokenizer.encode(continuation, add_special_tokens=False) + + tokens = question_tokenized + continuation_tokenized + + processed_sample = { + "input_ids": tokens, + "labels": [-100] * len(question_tokenized) + continuation_tokenized, + "attention_mask": [1] * len(tokens), + "position_ids": list(range(len(tokens))), + } + processed_samples.append(processed_sample) + + return processed_samples + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + # Process each sample and collect all results + all_processed_samples = [] + for sample in base_dataset: + processed_samples = process_dataset(sample) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + + random.shuffle(processed_dataset) + processed_dataset = [processed_dataset] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + # Process each sample and collect all results + all_processed_samples = [] + for sample in base_dataset: + processed_samples = process_dataset(sample) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + dataset = processed_dataset + return dataset + + +def get_graph_latent_cot_dataset_backtrack( + dataset_path, + frontier_stage, + r_current, + remember_rate, + configs, + tokenizer, +): + """Latent-CoT training set with cross-stage rehearsal / backtracking. + + Faithful port of the sudoku-latent-backtracking sampler + (``_sample_rehearsal_stage``): instead of building every example at the + frontier stage, each example is assigned a curriculum stage ``j`` (=latent + budget) drawn from a rehearsal distribution: + + * with prob ``remember_rate``: broad rehearsal, j ~ Uniform{1..frontier} + * otherwise: targeted backtracking, j ~ Uniform{r_current..frontier} + + ``r_current`` is the earliest stage whose retention dropped below the bar + (computed from the per-hop eval); when nothing has regressed it equals the + frontier, so sampling concentrates on the frontier as in vanilla curriculum. + """ + base_dataset = json.load(open(dataset_path)) + + if configs.debug: + base_dataset = base_dataset[:10000] + train_size = getattr(configs, "train_size", 0) + if train_size and train_size > 0: + base_dataset = base_dataset[:train_size] + + # Stage 0 is the no-latent hop-1 CoT warmup and MUST be trainable: flooring the + # frontier to 1 (as the sudoku port did) skips that foundation, so the model + # trains hop-2-with-latent from random init and never bootstraps (hop-1 acc + # froze at chance). Allow 0 so stage 0 is sampled like the vanilla builder. + frontier_stage = max(0, int(frontier_stage)) + r_current = min(max(0, int(r_current)), frontier_stage) + + def sample_stage(): + if random.random() < float(remember_rate): + return random.randint(0, frontier_stage) # broad rehearsal (incl. stage-0 foundation) + return random.randint(r_current, frontier_stage) # targeted backtracking + + def process_dataset(sample): + j = sample_stage() + if random.random() < configs.uniform_prob: + scheduled_stage_to_train = random.randint(0, min(j, len(sample["steps"]))) + else: + scheduled_stage_to_train = min(j, len(sample["steps"])) + + _smq = bool(getattr(configs, "stage_matched_q", False)) + expanded_data = expand_data( + sample, + scheduled_stage_to_train + 1, + len(sample["steps"]), + stage_matched_q=_smq, + ) + + processed_samples = [] + for question, continuation in [expanded_data]: + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + continuation_tokenized = tokenizer.encode(continuation, add_special_tokens=False) + tokens = question_tokenized + continuation_tokenized + processed_samples.append({ + "input_ids": tokens, + "labels": [-100] * len(question_tokenized) + continuation_tokenized, + "attention_mask": [1] * len(tokens), + "position_ids": list(range(len(tokens))), + }) + return processed_samples + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + all_processed_samples = [] + for sample in base_dataset: + all_processed_samples.extend(process_dataset(sample)) + random.shuffle(all_processed_samples) + processed_dataset = [all_processed_samples] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + all_processed_samples = [] + for sample in base_dataset: + all_processed_samples.extend(process_dataset(sample)) + random.shuffle(all_processed_samples) + dataset = all_processed_samples + return dataset + + +def get_graph_latent_question_dataset( + dataset_path, + scheduled_stage, + configs, + tokenizer, +): + base_dataset = json.load(open(dataset_path)) + if configs.debug: + base_dataset = base_dataset[:10000] + # similar to get_graph_latent_dataset, but we only keep the question + # without the continuation + + def process_dataset(sample, idx): + expanded_data = expand_data(sample, len(sample["steps"]) + 1, len(sample["steps"]), neg_sampling=False) + processed_samples = [] + for question, continuation in [expanded_data]: + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + processed_samples.append({ + "input_ids": question_tokenized, + "attention_mask": [1] * len(question_tokenized), + "position_ids": list(range(len(question_tokenized))), + "idx": idx, + }) + return processed_samples + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + # Process each sample and collect all results + all_processed_samples = [] + for idx, sample in enumerate(base_dataset): + processed_samples = process_dataset(sample, idx) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + processed_dataset = [processed_dataset] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + all_processed_samples = [] + for idx, sample in enumerate(base_dataset): + all_processed_samples.extend(process_dataset(sample, idx)) + random.shuffle(all_processed_samples) + dataset = all_processed_samples + + return dataset + +def get_graph_cot_dataset( + dataset_path, + configs, + tokenizer, +): + """ + Creates a dataset for training graph reasoning with chain of thought. + Each sample will contain the graph edges, question, and the full reasoning path to the answer. + """ + base_dataset = json.load(open(dataset_path)) + + if configs.debug: + base_dataset = base_dataset[:10000] + + def process_dataset(sample): + # Shuffle edges for robustness + random.shuffle(sample['edges']) + + # Construct the question part + question = " " + "|".join([f" {e[0]} {e[1]} " for e in sample['edges']]).strip() + " [Q] " + + # Randomly order target and neg_target in the question + if random.random() < 0.5: + question += f"{sample['target']} {sample['neg_target']}" + else: + question += f"{sample['neg_target']} {sample['target']}" + + question += f" [R] {sample['root']}" + + # Construct the chain of thought (optimal path) and answer + current_node = sample['root'] + continuation = "" + for i in range(1, 10): + if str(i) in sample["neighbor_k"]: + next_node = random.choice( + [n for n in sample["neighbor_k"][str(i)] if [current_node, n] in sample['edges']] + ) + continuation += f" {next_node}" + current_node = next_node + + continuation += f" [A] {sample['target']} " + + # Tokenize question and continuation + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + continuation_tokenized = tokenizer.encode(continuation, add_special_tokens=False) + + tokens = question_tokenized + continuation_tokenized + + processed_sample = { + "input_ids": tokens, + "labels": [-100] * len(question_tokenized) + continuation_tokenized, + "attention_mask": [1] * len(tokens), + "position_ids": list(range(len(tokens))), + } + + return [processed_sample] + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + # Process each sample and collect all results + all_processed_samples = [] + for sample in base_dataset: + processed_samples = process_dataset(sample) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + processed_dataset = [processed_dataset] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + # Process each sample and collect all results + all_processed_samples = [] + for sample in base_dataset: + processed_samples = process_dataset(sample) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + dataset = processed_dataset + + return dataset + +def get_graph_no_cot_dataset( + dataset_path, + configs, + tokenizer, +): + """ + Creates a dataset for training graph reasoning without chain of thought. + Each sample will contain the graph edges, question, and only the final answer + without intermediate reasoning steps. + """ + base_dataset = json.load(open(dataset_path)) + + if configs.debug: + base_dataset = base_dataset[:10000] + + def process_dataset(sample): + # Shuffle edges for robustness + random.shuffle(sample['edges']) + + # Construct the question part + question = " " + "|".join([f" {e[0]} {e[1]} " for e in sample['edges']]).strip() + " [Q] " + + # Randomly order target and neg_target in the question + if random.random() < 0.5: + question += f"{sample['target']} {sample['neg_target']}" + else: + question += f"{sample['neg_target']} {sample['target']}" + + question += f" [R] {sample['root']}" + + # Only include the answer without the reasoning path + continuation = f" [A] {sample['target']} " + + # Tokenize question and continuation + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + continuation_tokenized = tokenizer.encode(continuation, add_special_tokens=False) + + tokens = question_tokenized + continuation_tokenized + + processed_sample = { + "input_ids": tokens, + "labels": [-100] * len(question_tokenized) + continuation_tokenized, + "attention_mask": [1] * len(tokens), + "position_ids": list(range(len(tokens))), + } + + return [processed_sample] + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + # Process each sample and collect all results + all_processed_samples = [] + for sample in base_dataset: + processed_samples = process_dataset(sample) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + processed_dataset = [processed_dataset] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + # Process each sample and collect all results + all_processed_samples = [] + for sample in base_dataset: + processed_samples = process_dataset(sample) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + dataset = processed_dataset + + return dataset + +def get_graph_no_latent_question_dataset( + dataset_path, + configs, + tokenizer, +): + """ + Creates a dataset containing only the questions from the graph reasoning dataset, + without any latent tokens. Used for inference to get the input questions. + """ + base_dataset = json.load(open(dataset_path)) + if configs.debug: + base_dataset = base_dataset[:10000] + + def process_dataset(sample, idx): + # Construct the question part + random.shuffle(sample['edges']) + question = " " + "|".join([f" {e[0]} {e[1]} " for e in sample['edges']]).strip() + " [Q] " + + # Randomly order target and neg_target in the question + if random.random() < 0.5: + question += f"{sample['target']} {sample['neg_target']}" + else: + question += f"{sample['neg_target']} {sample['target']}" + + question += f" [R] {sample['root']}" + + # Tokenize the question + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + + processed_sample = { + "input_ids": question_tokenized, + "attention_mask": [1] * len(question_tokenized), + "position_ids": list(range(len(question_tokenized))), + "idx": idx, + } + return [processed_sample] + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + # Process each sample and collect all results + all_processed_samples = [] + for idx, sample in enumerate(base_dataset): + processed_samples = process_dataset(sample, idx) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + processed_dataset = [processed_dataset] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + # Process each sample and collect all results + all_processed_samples = [] + for idx, sample in enumerate(base_dataset): + processed_samples = process_dataset(sample, idx) + all_processed_samples.extend(processed_samples) + + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + dataset = processed_dataset + + return dataset + + +def get_graph_finalonly_dataset( + dataset_path, + scheduled_stage, + configs, + tokenizer, +): + """Final-only variant: at depth d (curriculum), form two depth-d candidates -- one + reachable (from neighbor_k), one unreachable (from neg_neighbor_k) -- give d latents, + and train the model to output the reachable one as the final answer ([A]). + + General over any two-component graph: reads only edges/root/neighbor_k/neg_neighbor_k + and len(steps). Standard vs BFS flavor is inherited from which frontier the data file + stores, exactly like get_graph_latent_cot_dataset.""" + base_dataset = json.load(open(dataset_path)) + + if configs.debug: + base_dataset = base_dataset[:10000] + + def process_dataset(sample): + L = len(sample["steps"]) + d = min(scheduled_stage + 1, L) # epoch-0 -> depth 1; reaches L at scheduled_stage L-1 + if random.random() < configs.uniform_prob: + d = random.randint(1, d) # mix in shallower depths + reach = str(random.choice(sample["neighbor_k"][str(d)])) + neg = str(random.choice(sample["neg_neighbor_k"][str(d)])) + + edges = list(sample["edges"]) + random.shuffle(edges) + prefix = " " + "|".join([f" {e[0]} {e[1]} " for e in edges]).strip() + " [Q] " + if random.random() < 0.5: + prefix += reach + " " + neg + else: + prefix += neg + " " + reach + prefix += " [R] " + str(sample["root"]) + question = prefix + " <|latent|>" * d + " [A] " + continuation = reach + + question_tokenized = tokenizer.encode(question, add_special_tokens=False) + continuation_tokenized = tokenizer.encode(continuation, add_special_tokens=False) + tokens = question_tokenized + continuation_tokenized + return [{ + "input_ids": tokens, + "labels": [-100] * len(question_tokenized) + continuation_tokenized, + "attention_mask": [1] * len(tokens), + "position_ids": list(range(len(tokens))), + }] + + if torch.cuda.device_count() > 1: + if dist.get_rank() == 0: + all_processed_samples = [] + for sample in base_dataset: + all_processed_samples.extend(process_dataset(sample)) + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + processed_dataset = [processed_dataset] + else: + processed_dataset = [None] + dist.broadcast_object_list(processed_dataset, src=0) + dataset = processed_dataset[0] + else: + all_processed_samples = [] + for sample in base_dataset: + all_processed_samples.extend(process_dataset(sample)) + processed_dataset = all_processed_samples + random.shuffle(processed_dataset) + dataset = processed_dataset + return dataset diff --git a/figs/attention_atlas/ablation_layers_0.json b/figs/attention_atlas/ablation_layers_0.json new file mode 100644 index 0000000000000000000000000000000000000000..88a8eeada96858d9ecc0d92914ffe59195cceb23 --- /dev/null +++ b/figs/attention_atlas/ablation_layers_0.json @@ -0,0 +1,267 @@ +{ + "ckpt": "ckpts/star-coconut-L10-bfs-backtrack-ce095/checkpoint_2000", + "n": 128, + "layers": "0", + "per_hop": { + "none": { + "1": { + "frontier": 1.0, + "ce_score": 0.9610676936857709, + "n": 128 + }, + "2": { + "frontier": 1.0, + "ce_score": 0.9578654705241544, + "n": 128 + }, + "3": { + "frontier": 1.0, + "ce_score": 0.9578002326721969, + "n": 128 + }, + "4": { + "frontier": 1.0, + "ce_score": 0.9603301003513176, + "n": 128 + }, + "5": { + "frontier": 1.0, + "ce_score": 0.9620351689466923, + "n": 128 + }, + "6": { + "frontier": 1.0, + "ce_score": 0.9613971536064091, + "n": 128 + }, + "7": { + "frontier": 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a/figs/interventions/pinlast_k_L10_L15.png b/figs/interventions/pinlast_k_L10_L15.png new file mode 100644 index 0000000000000000000000000000000000000000..d9e753add91082375fe063ace62c5526e178ae66 Binary files /dev/null and b/figs/interventions/pinlast_k_L10_L15.png differ diff --git a/figs/interventions/pinlast_k_L20.json b/figs/interventions/pinlast_k_L20.json new file mode 100644 index 0000000000000000000000000000000000000000..dc74ddc0c548fc34d00c9d5d448333157ef300de --- /dev/null +++ b/figs/interventions/pinlast_k_L20.json @@ -0,0 +1,140 @@ +{ + "CSO": { + "name": "CSO", + "ckpt": "ckpts/L20_cso_prom090/checkpoint_200", + "clean": 0.53125, + "ks": [ + 1, + 2, + 3, + 5, + 7, + 10, + 14, + 19 + ], + "rand_mean": [ + 0.5244140625, + 0.525390625, + 0.5224609375, + 0.5244140625, + 0.5244140625, + 0.5224609375, + 0.521484375, + 0.5234375 + ], + "near": [ + 0.5390625, + 0.5234375, + 0.5234375, + 0.5234375, + 0.5234375, + 0.5234375, + 0.5234375, + 0.515625 + ], + "first": [ + 0.5234375, + 0.5234375, + 0.53125, + 0.5234375, + 0.5234375, + 0.53125, + 0.5234375, + 0.515625 + ], + "all": 0.53125 + }, + "BT W=5": { + "name": "BT W=5", + "ckpt": "ckpts/L20_w5_s1_prom090_bt090/checkpoint_225", + "clean": 1.0, + "ks": [ + 1, + 2, + 3, + 5, + 7, + 10, + 14, + 19 + ], + "rand_mean": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + "near": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + "first": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + "all": 1.0 + }, + "BT W=2": { + "name": "BT W=2", + "ckpt": "ckpts/L20_w2_s1_prom090_bt090/checkpoint_225", + "clean": 0.9296875, + "ks": [ + 1, + 2, + 3, + 5, + 7, + 10, + 14, + 19 + ], + "rand_mean": [ + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9287109375, + 0.9296875, + 0.921875 + ], + "near": [ + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.921875 + ], + "first": [ + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875, + 0.9296875 + ], + "all": 0.9296875 + } +} \ No newline at end of file diff --git a/figs/interventions/pinlast_k_L20.png b/figs/interventions/pinlast_k_L20.png new file mode 100644 index 0000000000000000000000000000000000000000..3a0b7a3a9a04ccd6465f4d3fc8c19177fe0dbe33 Binary files /dev/null and b/figs/interventions/pinlast_k_L20.png differ diff --git a/figs/interventions/pinlast_k_L20_table.png b/figs/interventions/pinlast_k_L20_table.png new file mode 100644 index 0000000000000000000000000000000000000000..bf3476d0fedf782cfb01788f4f985980f8fcaebe Binary files /dev/null and b/figs/interventions/pinlast_k_L20_table.png differ diff --git a/gen_L3_100k.py b/gen_L3_100k.py new file mode 100644 index 0000000000000000000000000000000000000000..3909beb0099ea6e6db22b29c90a8d05679fca694 --- /dev/null +++ b/gen_L3_100k.py @@ -0,0 +1,13 @@ +import json, random, sys +sys.path.insert(0, "/scratch/users/gatmiry/reasoning-by-superposition") +from generate_2arm_star import gen, to_sample +L = 3 +rng = random.Random(0); seen = set() +val = gen(L, 2000, rng, seen) +train = gen(L, 100000, rng, seen) +for split, stars in [("valid", val), ("train", train)]: + json.dump([to_sample(s, "coconut") for s in stars], + open(f"data/star_L3_100k_{split}_fo_coconut.json", "w")) + print(f"{split}: {len(stars)}") +s = to_sample(val[0], "coconut") +print("check: steps len", len(s["steps"]), "neighbor_k keys", sorted(s["neighbor_k"]), "nodes", 2 + 4 * L) diff --git a/gen_L6_100k.py b/gen_L6_100k.py new file mode 100644 index 0000000000000000000000000000000000000000..00e137fe9ae19b7b9db92c05894948fffe88f0c4 --- /dev/null +++ b/gen_L6_100k.py @@ -0,0 +1,14 @@ +import json, random, sys +sys.path.insert(0, "/scratch/users/gatmiry/reasoning-by-superposition") +from generate_2arm_star import gen, to_sample +L = 6 +rng = random.Random(0); seen = set() +val = gen(L, 2000, rng, seen) # held-out val first (fixed) +train = gen(L, 100000, rng, seen) # 100k, deduped vs val +for split, stars in [("valid", val), ("train", train)]: + json.dump([to_sample(s, "coconut") for s in stars], + open(f"data/star_L6_100k_{split}_fo_coconut.json", "w")) + print(f"{split}: {len(stars)} samples") +s = to_sample(val[0], "coconut") +print("check: steps len", len(s["steps"]), "neighbor_k keys", sorted(s["neighbor_k"]), + "neg_neighbor_k keys", sorted(s["neg_neighbor_k"])) diff --git a/gen_d1_3m.py b/gen_d1_3m.py new file mode 100644 index 0000000000000000000000000000000000000000..bdfcfeffee0afdfa2ecfcd274b5f9659d920a60a --- /dev/null +++ b/gen_d1_3m.py @@ -0,0 +1,11 @@ +import json, random, sys +sys.path.insert(0, "/scratch/users/gatmiry/reasoning-by-superposition") +from generate_2arm_star import gen, to_sample +L = 1 +rng = random.Random(0); seen = set() +gen(L, 2000, rng, seen) # reproduce val consumption (seed 0) +gen(L, 960000, rng, seen) # reproduce 960k-train consumption +train3m = gen(L, 3000000, rng, seen) # next 3M, deduped vs val + all prior train +json.dump([to_sample(s, "coconut") for s in train3m], + open("data/star_d1_train3000000_fo_coconut.json", "w")) +print("3M written:", len(train3m)) diff --git a/gen_d1_sweep.py b/gen_d1_sweep.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb9430ec7b59f587dd4999a24ee948eeda80418 --- /dev/null +++ b/gen_d1_sweep.py @@ -0,0 +1,20 @@ +import json, random, sys +sys.path.insert(0, "/scratch/users/gatmiry/reasoning-by-superposition") +from generate_2arm_star import gen, to_sample # gen(L,n,rng,seen)->stars ; to_sample(star,flavor) + +L = 1 +rng = random.Random(0) +seen = set() +val_stars = gen(L, 2000, rng, seen) # fixed held-out val (generated first, seed-determined) +train_stars = gen(L, 960000, rng, seen) # deduped vs val (shared `seen`); nested prefixes below + +def write(stars, path): + json.dump([to_sample(s, "coconut") for s in stars], open(path, "w")) + +write(val_stars, "data/star_d1_valid_fo_coconut.json") +for n in [14000, 60000, 240000, 960000]: + write(train_stars[:n], f"data/star_d1_train{n}_fo_coconut.json") + print("wrote train", n) +print("val", len(val_stars), "| sample keys:", sorted(to_sample(val_stars[0], "coconut").keys())) +s0 = to_sample(val_stars[0], "coconut") +print("example: root", s0["root"], "neighbor_k[1]", s0["neighbor_k"]["1"], "neg_neighbor_k[1]", s0["neg_neighbor_k"]["1"], "edges", s0["edges"]) diff --git a/gen_overnight.py b/gen_overnight.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3d1ad500cf85bc016c3eb870826b6d65b225a6 --- /dev/null +++ b/gen_overnight.py @@ -0,0 +1,18 @@ +import json, random, sys +sys.path.insert(0, "/scratch/users/gatmiry/reasoning-by-superposition") +from generate_2arm_star import gen, to_sample + +# L=1 30k: reproduce val consumption then 30k train (nested w/ existing 14k/60k, same val) +rng = random.Random(0); seen = set() +gen(1, 2000, rng, seen) +tr30 = gen(1, 30000, rng, seen) +json.dump([to_sample(s, "coconut") for s in tr30], open("data/star_d1_train30000_fo_coconut.json", "w")) +print("L1 30k train:", len(tr30)) + +# L=2 fresh: val 2000 + train 100k (10 nodes, 8 edges) +r2 = random.Random(0); s2 = set() +v2 = gen(2, 2000, r2, s2) +t2 = gen(2, 100000, r2, s2) +json.dump([to_sample(s, "coconut") for s in v2], open("data/star_L2_valid_fo_coconut.json", "w")) +json.dump([to_sample(s, "coconut") for s in t2], open("data/star_L2_train100k_fo_coconut.json", "w")) +print("L2 val/train:", len(v2), len(t2), "| nodes", 2 + 4 * 2, "| neighbor_k[1..2]:", {k: v2[0]["neighbor_k"][k] for k in ["1", "2"]}) diff --git a/generate_2arm_star.py b/generate_2arm_star.py new file mode 100644 index 0000000000000000000000000000000000000000..0bb77c35c181f914f595350490bba644aadea3f7 --- /dev/null +++ b/generate_2arm_star.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +"""Generate a 2-arm star reachability dataset in the rbs (reasoning-by-superposition) format. + +Two disconnected, structurally identical 2-arm stars: + - reachable component (root R): arms A, B; one leaf is `target`. + - unreachable component (root R2): arms C, D; one leaf is `neg_target` (unreachable from R). + +Emits per split BOTH flavors (identical graphs, differing only in the frontier fields): + *_fo_coconut.json : neighbor_k[k]=[target-arm node], neg_neighbor_k[k]=[neg-arm node] (unique) + *_fo_bfs.json : neighbor_k[k]=[both reachable arms], neg_neighbor_k[k]=[both neg arms] (full) + +Fields (general rbs contract + the two new symmetric neg-side fields consumed by the +final-only variant): edges, root, target, neg_target, neighbor_k, neg_root, neg_neighbor_k, +steps, idx_to_symbol, difficulty, question, answer. + +Node ids are integers 0..30 (the STokenizer vocab), assigned at random per sample. +""" +import json, random, argparse + +try: + from stokenizer import NUM_NODES as VOCAB_NODES # single source of truth +except Exception: + VOCAB_NODES = 100 # STokenizer node ids: "0".."99" + + +def _arm(node_iter, root, L): + arm, edges, prev = [], [], root + for _ in range(L): + x = next(node_iter) + edges.append([prev, x]) + prev = x + arm.append(x) + return arm, edges + + +def make_star(L, rng, node_pool=None): + """Raw 2-arm-star structure (flavor-independent). + + node_pool: size of the id pool to sample node labels from (ids 0..node_pool-1). + The original (working) L6 dataset used a dense pool of 31 ids for 26 nodes, so + every id appeared in nearly every sample. Sampling from the full 100-id vocab + makes ids sparse/rare and empirically breaks stage-0 learning. Default: dense + pool = nodes needed + 5 margin (matching the original L6 ratio). + """ + n_nodes = 2 + 4 * L + if node_pool is None: + node_pool = min(n_nodes + 5, VOCAB_NODES) + assert n_nodes <= node_pool <= VOCAB_NODES + ids = rng.sample(range(node_pool), n_nodes) + it = iter(ids) + R = next(it) + armA, eA = _arm(it, R, L) + armB, eB = _arm(it, R, L) + R2 = next(it) + armC, eC = _arm(it, R2, L) + armD, eD = _arm(it, R2, L) + tgt_arm = rng.choice([armA, armB]) # reachable arm holding target + neg_arm = rng.choice([armC, armD]) # unreachable arm holding neg_target + edges = eA + eB + eC + eD + rng.shuffle(edges) + return { + "R": R, "R2": R2, + "armA": armA, "armB": armB, "armC": armC, "armD": armD, + "tgt_arm": tgt_arm, "neg_arm": neg_arm, + "edges": edges, + "L": L, + } + + +def to_sample(star, flavor): + """Serialize a star into an rbs sample for the given flavor ('coconut' or 'bfs').""" + L = star["L"] + R, R2 = star["R"], star["R2"] + tgt_arm, neg_arm = star["tgt_arm"], star["neg_arm"] + target, neg_target = tgt_arm[-1], neg_arm[-1] + + neighbor_k = {"0": [R]} + neg_neighbor_k = {"0": [R2]} + for k in range(1, L + 1): + if flavor == "coconut": # unique shortest-path frontier + neighbor_k[str(k)] = [tgt_arm[k - 1]] + neg_neighbor_k[str(k)] = [neg_arm[k - 1]] + else: # bfs: full frontier (both arms) + neighbor_k[str(k)] = [star["armA"][k - 1], star["armB"][k - 1]] + neg_neighbor_k[str(k)] = [star["armC"][k - 1], star["armD"][k - 1]] + + all_nodes = sorted([R, R2] + star["armA"] + star["armB"] + star["armC"] + star["armD"]) + return { + "question": "", + "answer": str(target), + "steps": [str(x) for x in tgt_arm], # length L (rbs uses only len()) + "edges": star["edges"], + "root": R, + "target": target, + "neg_target": neg_target, + "neighbor_k": neighbor_k, + "neg_root": R2, + "neg_neighbor_k": neg_neighbor_k, + "idx_to_symbol": [str(x) for x in all_nodes], + "difficulty": L, + } + + +def gen(L, n, rng, seen, node_pool=None): + out = [] + while len(out) < n: + star = make_star(L, rng, node_pool=node_pool) + key = (star["R"], star["tgt_arm"][-1], star["neg_arm"][-1], + frozenset(map(tuple, star["edges"]))) + if key in seen: + continue + seen.add(key) + out.append(star) + return out + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--L", type=int, default=6, help="arm length = reasoning depth") + ap.add_argument("--n_train", type=int, default=14000) + ap.add_argument("--n_valid", type=int, default=256) + ap.add_argument("--n_test", type=int, default=256) + ap.add_argument("--seed", type=int, default=0) + ap.add_argument("--out_prefix", default="data/star_2arm_L") + ap.add_argument("--node_pool", type=int, default=None, + help="sample node ids from 0..node_pool-1 (default: nodes+5, " + "the dense pool the original working L6 data used)") + a = ap.parse_args() + assert 2 + 4 * a.L <= VOCAB_NODES, f"L={a.L} needs {2 + 4 * a.L} nodes > {VOCAB_NODES}" + rng = random.Random(a.seed) + pool = a.node_pool if a.node_pool is not None else min(2 + 4 * a.L + 5, VOCAB_NODES) + seen = set() # dedup across train/valid/test (no leakage) + for split, n in [("train", a.n_train), ("valid", a.n_valid), ("test", a.n_test)]: + stars = gen(a.L, n, rng, seen, node_pool=pool) + for flavor in ("coconut", "bfs"): + data = [to_sample(s, flavor) for s in stars] + path = f"{a.out_prefix}{a.L}_{split}_fo_{flavor}.json" + json.dump(data, open(path, "w")) + print(f"{path}: {len(data)} samples, {2 + 4 * a.L} nodes/sample, " + f"{4 * a.L} edges/sample, node_pool={pool}") + + +if __name__ == "__main__": + main() diff --git a/generate_star.py b/generate_star.py new file mode 100644 index 0000000000000000000000000000000000000000..58bd8d761a5fd78011579817a548cba20a5a2e56 --- /dev/null +++ b/generate_star.py @@ -0,0 +1,173 @@ +#!/usr/bin/env python3 +"""Generate a B-branch star reachability dataset in the rbs format. + +Generalises generate_2arm_star.py from a hardcoded 2 arms to any branch count B. + +Two disconnected, structurally identical B-branch stars: + - reachable component (root R): branches 0..B-1; one leaf is `target`. + - unreachable component (root R2): branches 0..B-1; one leaf is `neg_target`. + +The reachable frontier at hop k is therefore B nodes (one per branch), so the +balance score in graph_metrics -- ce_score = exp(log|F| - CE) = |F| * GM(p) -- +is 1.0 only when all B branches hold probability 1/B. Nothing in the metric or +the curriculum assumes |F| == 2, so B > 2 works with no training-code change. + +Node budget: 2 components * B branches * L nodes, plus the two roots, so + n_nodes = 2 + 2*B*L +must fit the tokenizer (node id == token id, NUM_NODES ids available). That is +the binding constraint: B*L <= (NUM_NODES - 2) / 2, i.e. 49 at NUM_NODES=100. + +Emits both flavors per split (identical graphs, differing only in frontier fields): + *_fo_coconut.json : neighbor_k[k] = [target-branch node] (unique path) + *_fo_bfs.json : neighbor_k[k] = [node from every branch] (full frontier) + +Example: + python generate_star.py --B 4 --L 10 --n_train 100000 \ + --out_prefix data/star_4arm_L +""" +import json, random, argparse + +try: + from stokenizer import NUM_NODES as VOCAB_NODES # single source of truth +except Exception: + VOCAB_NODES = 100 + + +def _branch(node_iter, root, L): + """One chain of L nodes hanging off `root`.""" + nodes, edges, prev = [], [], root + for _ in range(L): + x = next(node_iter) + edges.append([prev, x]) + prev = x + nodes.append(x) + return nodes, edges + + +def make_star(B, L, rng, node_pool=None): + """Raw B-branch double-star structure (flavor-independent). + + node_pool: size of the id pool to sample labels from (ids 0..node_pool-1). + A dense pool matters: sampling from the full 100-id vocab when a graph only + uses ~30 ids makes individual ids rare and empirically breaks stage-0 + learning. Default keeps the original dense ratio (nodes needed + 5). + """ + n_nodes = 2 + 2 * B * L + if node_pool is None: + node_pool = min(n_nodes + 5, VOCAB_NODES) + assert n_nodes <= node_pool <= VOCAB_NODES, ( + f"B={B} L={L} needs {n_nodes} nodes; pool={node_pool} vocab={VOCAB_NODES}" + ) + ids = rng.sample(range(node_pool), n_nodes) + it = iter(ids) + + R = next(it) + pos_branches, edges = [], [] + for _ in range(B): + nodes, e = _branch(it, R, L) + pos_branches.append(nodes) + edges += e + + R2 = next(it) + neg_branches = [] + for _ in range(B): + nodes, e = _branch(it, R2, L) + neg_branches.append(nodes) + edges += e + + tgt_branch = rng.choice(pos_branches) + neg_branch = rng.choice(neg_branches) + rng.shuffle(edges) + return { + "R": R, "R2": R2, + "pos_branches": pos_branches, "neg_branches": neg_branches, + "tgt_branch": tgt_branch, "neg_branch": neg_branch, + "edges": edges, "L": L, "B": B, + } + + +def to_sample(star, flavor): + L = star["L"] + R, R2 = star["R"], star["R2"] + tgt_branch, neg_branch = star["tgt_branch"], star["neg_branch"] + target, neg_target = tgt_branch[-1], neg_branch[-1] + + neighbor_k = {"0": [R]} + neg_neighbor_k = {"0": [R2]} + for k in range(1, L + 1): + if flavor == "coconut": # unique shortest-path frontier + neighbor_k[str(k)] = [tgt_branch[k - 1]] + neg_neighbor_k[str(k)] = [neg_branch[k - 1]] + else: # bfs: every branch at depth k + neighbor_k[str(k)] = [b[k - 1] for b in star["pos_branches"]] + neg_neighbor_k[str(k)] = [b[k - 1] for b in star["neg_branches"]] + + all_nodes = sorted( + [R, R2] + + [n for b in star["pos_branches"] for n in b] + + [n for b in star["neg_branches"] for n in b] + ) + return { + "question": "", + "answer": str(target), + "steps": [str(x) for x in tgt_branch], # length L (rbs uses only len()) + "edges": star["edges"], + "root": R, + "target": target, + "neg_target": neg_target, + "neighbor_k": neighbor_k, + "neg_root": R2, + "neg_neighbor_k": neg_neighbor_k, + "idx_to_symbol": [str(x) for x in all_nodes], + "difficulty": L, + } + + +def gen(B, L, n, rng, seen, node_pool=None): + out = [] + while len(out) < n: + star = make_star(B, L, rng, node_pool=node_pool) + key = (star["R"], star["tgt_branch"][-1], star["neg_branch"][-1], + frozenset(map(tuple, star["edges"]))) + if key in seen: + continue + seen.add(key) + out.append(star) + return out + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--B", type=int, default=2, help="branches per component") + ap.add_argument("--L", type=int, default=10, help="branch length = reasoning depth") + ap.add_argument("--n_train", type=int, default=14000) + ap.add_argument("--n_valid", type=int, default=256) + ap.add_argument("--n_test", type=int, default=256) + ap.add_argument("--seed", type=int, default=0) + ap.add_argument("--out_prefix", default=None, + help="default: data/star_{B}arm_L") + ap.add_argument("--node_pool", type=int, default=None, + help="sample ids from 0..node_pool-1 (default: nodes+5, dense)") + a = ap.parse_args() + + n_nodes = 2 + 2 * a.B * a.L + assert n_nodes <= VOCAB_NODES, ( + f"B={a.B} L={a.L} needs {n_nodes} nodes > vocab {VOCAB_NODES}; " + f"max B*L is {(VOCAB_NODES - 2) // 2}" + ) + prefix = a.out_prefix or f"data/star_{a.B}arm_L" + rng = random.Random(a.seed) + pool = a.node_pool if a.node_pool is not None else min(n_nodes + 5, VOCAB_NODES) + seen = set() # dedup across splits (no leakage) + for split, n in [("train", a.n_train), ("valid", a.n_valid), ("test", a.n_test)]: + stars = gen(a.B, a.L, n, rng, seen, node_pool=pool) + for flavor in ("coconut", "bfs"): + data = [to_sample(s, flavor) for s in stars] + path = f"{prefix}{a.L}_{split}_fo_{flavor}.json" + json.dump(data, open(path, "w")) + print(f"{path}: {len(data)} samples, B={a.B}, {n_nodes} nodes/sample, " + f"{2 * a.B * a.L} edges/sample, frontier={a.B}/hop, node_pool={pool}") + + +if __name__ == "__main__": + main() diff --git a/graph_metrics.py b/graph_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..7c17a57189af80d420dc716d601689401fee215a --- /dev/null +++ b/graph_metrics.py @@ -0,0 +1,267 @@ +"""General graph-reasoning evaluation metrics for the symbolic graph-reachability task. + +For each hop distance ``k`` we score the model's next-token prediction (after ``k-1`` +latents) into the paper's categories, with distances measured over the graph from the +root: + + - reachable: dist(root -> pred) <= k + - frontier: dist(root -> pred) == k + - optimal: dist(root -> pred) == k AND pred is on a shortest path to the target + (i.e. dist(root->pred) + dist(pred->target) == dist(root->target)) + +Categories are computed directly from (edges, root, target) — present in every data +file — so they are independent of which ``neighbor_k`` a variant's data ships and are +directly comparable across the standard / BFS Coconut variants. +""" +import json +import random +import collections + +import torch +import torch.distributed as dist +from datasets import Dataset +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from dataset import expand_data + + +def _bfs(adj, src): + """Distances from src over a directed adjacency dict (unreached nodes absent).""" + dist_map = {src: 0} + q = collections.deque([src]) + while q: + u = q.popleft() + for v in adj[u]: + if v not in dist_map: + dist_map[v] = dist_map[u] + 1 + q.append(v) + return dist_map + + +def _distances(edges, root, target): + """(fdist: root->node, bdist: node->target, L: shortest root->target).""" + fwd = collections.defaultdict(list) + bwd = collections.defaultdict(list) + for a, b in edges: + fwd[a].append(b) + bwd[b].append(a) + fdist = _bfs(fwd, root) + bdist = _bfs(bwd, target) + return fdist, bdist, fdist.get(target) + + +def _classify(g, k, fdist, bdist, L): + """(reachable, frontier, optimal) booleans for predicted node id g at hop k.""" + d = fdist.get(g) + if d is None: # not a reachable node (or a special token) + return False, False, False + reachable = d <= k + frontier = d == k + optimal = frontier and L is not None and (g in bdist) and (d + bdist[g] == L) + return reachable, frontier, optimal + + +def _build_items(data_path, tokenizer, max_samples=None, stage_matched_q=False): + base = json.load(open(data_path)) + if max_samples is not None and max_samples < len(base): + base = random.Random(0).sample(base, max_samples) # fixed subset across epochs + items, meta = [], [] + for s in base: + L_steps = len(s["steps"]) + fdist, bdist, L = _distances(s["edges"], s["root"], s["target"]) + for k in range(1, L_steps + 1): + q, _ = expand_data(s, k, L_steps, stage_matched_q=stage_matched_q) # prefix + (k-1) latents + qtok = tokenizer.encode(q, add_special_tokens=False) + meta.append((k, fdist, bdist, L)) + items.append({ + "input_ids": qtok, + "attention_mask": [1] * len(qtok), + "position_ids": list(range(len(qtok))), + "idx": len(items), + }) + return Dataset.from_list(items), meta + + +def perhop_categorize(parallel_model, data_path, tokenizer, collator, rank, max_samples=None, stage_matched_q=False): + """Per hop k metrics (DDP-reduced). + + Returns {k: {reachable, frontier, optimal, superposition}} where: + reachable/frontier/optimal — same as before (from argmax next-token) + superposition — fraction of graphs where the top-|F| logits equal exactly + the reachable frontier set F = {n : dist(root,n) == k}. For the 2-arm + star |F|=2, so this is "top-2 tokens == both hop-k nodes". + ce_score — exp(log|F| - CE) where CE is the cross-entropy against + Uniform(F), i.e. the graded version of `superposition`. 1.0 means the + model is exactly at the log|F| floor (perfectly balanced over both arms) + and it falls off as either arm loses probability. Unlike `superposition` + this sees magnitude, not just rank. + """ + ds, meta = _build_items( + data_path, tokenizer, max_samples=max_samples, stage_matched_q=stage_matched_q + ) + max_k = max(k for k, *_ in meta) + dl = DataLoader( + ds, num_workers=1, pin_memory=True, batch_size=1, + collate_fn=collator, sampler=DistributedSampler(ds, shuffle=False), + ) + reach = torch.zeros(max_k + 1, device=rank) + front = torch.zeros(max_k + 1, device=rank) + opt = torch.zeros(max_k + 1, device=rank) + superpos = torch.zeros(max_k + 1, device=rank) + ce_score = torch.zeros(max_k + 1, device=rank) + tot = torch.zeros(max_k + 1, device=rank) + parallel_model.module.eval() + with torch.no_grad(): + for batch in dl: + i = int(batch["idx"][0]) + k, fdist, bdist, L = meta[i] + input_ids = batch["input_ids"].to(rank) + # Same forward path as Coconut.generate (max_new_tokens=1), but we + # keep the full logit vector so we can score top-|F| == frontier set. + labels = input_ids.clone() + position_ids = torch.arange( + 0, input_ids.shape[1], dtype=torch.long, device=rank + ).reshape(1, -1) + outputs = parallel_model.module.forward( + input_ids, + torch.ones_like(input_ids, device=rank), + labels, + position_ids, + ) + logits = outputs.logits[0, -1] + g = int(torch.argmax(logits).item()) + r, f, o = _classify(g, k, fdist, bdist, L) + tot[k] += 1; reach[k] += r; front[k] += f; opt[k] += o + F = {n for n, d in fdist.items() if d == k} + if F: + top = torch.topk(logits, k=len(F)).indices.tolist() + if set(int(t) for t in top) == F: + superpos[k] += 1 + # Soft superposition. The training target at hop k is a uniformly + # random element of F, so E[-log p(y)] = -log GM(p over F), whose + # floor is log|F|. Using the GEOMETRIC mean is what makes this + # sensitive to a collapsed arm: top-|F| set equality is rank-only + # and scores p=(.98,.001) the same as p=(.5,.5), and sum-of-mass is + # an arithmetic mean that also misses the asymmetry. + # exp(log|F| - ce) maps the floor to 1.0 and decreases from there, + # so it is directionally compatible with the accuracy thresholds. + logp = torch.log_softmax(logits.float(), dim=-1) + ce = -torch.stack([logp[n] for n in F]).mean() + ce_score[k] += torch.exp( + torch.log(torch.tensor(float(len(F)), device=logits.device)) - ce + ).clamp(max=1.0) + for t in (reach, front, opt, superpos, ce_score, tot): + dist.all_reduce(t, op=dist.ReduceOp.SUM) + res = {} + for k in range(1, max_k + 1): + n = tot[k].item() + if n > 0: + res[k] = { + "reachable": reach[k].item() / n, + "frontier": front[k].item() / n, + "optimal": opt[k].item() / n, + "superposition": superpos[k].item() / n, + "ce_score": ce_score[k].item() / n, + } + return res + + +def category_log_dict(prefix, cats, acc_key): + """Flatten per-hop cats into wandb keys, aliasing acc_key as acc_hop*.""" + out = {} + for k, c in cats.items(): + out[f"{prefix}/reachable_hop{k}"] = c["reachable"] + out[f"{prefix}/frontier_hop{k}"] = c["frontier"] + out[f"{prefix}/optimal_hop{k}"] = c["optimal"] + if "superposition" in c: + out[f"{prefix}/superposition_hop{k}"] = c["superposition"] + if "ce_score" in c: + out[f"{prefix}/ce_score_hop{k}"] = c["ce_score"] + out[f"{prefix}/acc_hop{k}"] = c[acc_key] + return out + + +def _build_finalonly_items(data_path, tokenizer, max_samples=None): + """Per (sample, depth k) final-only eval prompt: + [Q] {reach,neg}(seeded order) [R] root <|latent|>*k [A] -> should emit reach. + Deterministic candidate choice (index 0 of each frontier) for reproducibility. + meta carries (k, reach, fdist, bdist, L) so the emitted [A] token can also be + classified reachable/frontier/optimal by its distance from root.""" + base = json.load(open(data_path)) + if max_samples is not None and max_samples < len(base): + base = random.Random(0).sample(base, max_samples) + items, meta = [], [] + for s in base: + L = len(s["steps"]) + fdist, bdist, Ld = _distances(s["edges"], s["root"], s["target"]) + for k in range(1, L + 1): + reach = s["neighbor_k"][str(k)][0] + neg = s["neg_neighbor_k"][str(k)][0] + cands = [reach, neg] + random.Random(len(items)).shuffle(cands) # deterministic, position-unbiased + q = (" " + "|".join([f" {e[0]} {e[1]} " for e in s["edges"]]).strip() + + " [Q] " + str(cands[0]) + " " + str(cands[1]) + + " [R] " + str(s["root"]) + " <|latent|>" * k + " [A] ") + qtok = tokenizer.encode(q, add_special_tokens=False) + meta.append((k, reach, fdist, bdist, Ld)) + items.append({ + "input_ids": qtok, + "attention_mask": [1] * len(qtok), + "position_ids": list(range(len(qtok))), + "idx": len(items), + }) + return Dataset.from_list(items), meta + + +def finalonly_categorize(parallel_model, data_path, tokenizer, collator, rank, max_samples=None): + """Per depth k over the final-only [A] prediction (DDP-reduced), returns + {k: {"acc","reachable","frontier","optimal"}}: + acc = emitted token == the designated reachable candidate (the trained target) + reachable = emitted node is reachable from root within k hops (dist <= k) + frontier = emitted node at distance exactly k from root + optimal = frontier AND on a shortest path to target + (acc - reachable = picked a different reachable node; the unreachable candidate + classifies as none of the three.)""" + ds, meta = _build_finalonly_items(data_path, tokenizer, max_samples=max_samples) + max_k = max(k for k, *_ in meta) + dl = DataLoader( + ds, num_workers=1, pin_memory=True, batch_size=1, + collate_fn=collator, sampler=DistributedSampler(ds, shuffle=False), + ) + acc = torch.zeros(max_k + 1, device=rank) + reach_t = torch.zeros(max_k + 1, device=rank) + front = torch.zeros(max_k + 1, device=rank) + opt = torch.zeros(max_k + 1, device=rank) + tot = torch.zeros(max_k + 1, device=rank) + parallel_model.module.eval() + with torch.no_grad(): + for batch in dl: + i = int(batch["idx"][0]) + k, reach, fdist, bdist, Ld = meta[i] + inp = {kk: v.to(rank) for kk, v in batch.items() + if v is not None and kk not in ["idx", "position_ids"]} + out = parallel_model.module.generate( + **inp, max_new_tokens=1, synced_gpus=True, + eos_token_id=tokenizer.eos_token_id, + ) + g = int(out[0, -1].item()) + r, f, o = _classify(g, k, fdist, bdist, Ld) + tot[k] += 1 + acc[k] += int(g == reach) + reach_t[k] += r + front[k] += f + opt[k] += o + for t in (acc, reach_t, front, opt, tot): + dist.all_reduce(t, op=dist.ReduceOp.SUM) + res = {} + for k in range(1, max_k + 1): + n = tot[k].item() + if n > 0: + res[k] = { + "acc": acc[k].item() / n, + "reachable": reach_t[k].item() / n, + "frontier": front[k].item() / n, + "optimal": opt[k].item() / n, + } + return res diff --git a/iclr_subsection_latent_backtracking.tex b/iclr_subsection_latent_backtracking.tex new file mode 100644 index 0000000000000000000000000000000000000000..02e11de271c35b767d2798195d401f68ed68a9e0 --- /dev/null +++ b/iclr_subsection_latent_backtracking.tex @@ -0,0 +1,252 @@ +% ===================================================================== +% Standalone, self-compiling version of the ICLR subsection +% "Retention-Gated Backtracking on Star Graphs". +% +% Compile: pdflatex iclr_subsection_latent_backtracking.tex +% Requires: figs/panelA_stagewise_learning.png +% figs/panelE_superposition.png +% +% Numbering is pinned so the subsection renders as 5.5, exactly as it +% would inside the host paper. Cross-references to sections of the host +% paper (5.2, 5.3, 5.4) are pre-resolved below, so this compiles with no +% undefined references. To drop into the real paper, delete the preamble +% and the "PRE-RESOLVED CROSS-REFERENCES" block and keep everything from +% \subsection onward. +% ===================================================================== + +\documentclass[10pt]{article} + +\usepackage[letterpaper,margin=1in]{geometry} +\usepackage{amsmath,amssymb} +\usepackage{booktabs} +\usepackage{graphicx} +\usepackage[T1]{fontenc} +\usepackage{times} +\usepackage[hidelinks]{hyperref} + +\graphicspath{{figs/}{./}} +\setlength{\parskip}{0.25em} + +% ---- PRE-RESOLVED CROSS-REFERENCES (delete when merging into paper) ---- +% Lets the two references to earlier experiments resolve without the host +% paper being present. The 5-field form is hyperref-compatible and also +% degrades correctly if hyperref is removed. +\newlabel{sec:graph-reasoning}{{5.2}{1}{}{}{}} +\newlabel{sec:sudoku}{{5.3}{1}{}{}{}} +% ----------------------------------------------------------------------- + +% Pin numbering so this renders as Section 5.5, as in the host paper. +\setcounter{section}{5} +\setcounter{subsection}{4} +\setcounter{table}{2} +\setcounter{figure}{7} + +\begin{document} + +\subsection{Retention-Gated Backtracking on Star Graphs} +\label{sec:latent-backtracking} + +We train a two-layer transformer from scratch on a graph reachability task using +a staged latent curriculum, where stage $i$ supplies the model with $i$ latent +thought tokens and supervises the node reached after $i+1$ hops. Alongside the +promotion rule that advances the curriculum, we add a second rule that evaluates +every hop the model has already mastered and sends training back to any hop +whose performance has dropped below a threshold. We compare three criteria for +that second rule against two controls that lack it, and we then decode the +latent tokens through the language modelling head to determine what each one +encodes. The three criteria yield final task accuracies ranging from $0.680$ to +$0.988$ and latent representations differing by an order of magnitude in +quality, yet they are nearly indistinguishable under the per-hop accuracy that +one would naturally monitor to detect forgetting. + +\subsubsection{Task Description} +\label{sec:bt-task} + +Each instance contains two disconnected components: a root $r$ with two disjoint +chains of length $L$, and a structurally identical copy with disjoint node +identifiers. The model observes the shuffled edge lists of both components, the +root, and two candidate leaves drawn one from each component, and must emit the +leaf reachable from $r$; the candidate from the second component, the decoy, is +never reachable. The reachable frontier at hop $k$, +$F_k=\{v:\mathrm{dist}(r,v)=k\}$, contains exactly two nodes, one per arm, and +both remain consistent with the prompt until the final answer disambiguates them. +A model performing a genuine traversal must therefore carry both forward at every +step, whereas one that commits to a single arm has replaced search with a guess +that succeeds half the time. This distinction is decidable at every hop, unlike +the random-tree task of Section~\ref{sec:graph-reasoning}, where the frontier is +a singleton and a correct prediction is indistinguishable from a correct +traversal. We report results at $L=10$, where each instance has $42$ nodes and +$40$ edges, of which ten lie on the path to the answer. + +\subsubsection{Instantiating ATC with Retention Gates} +\label{sec:bt-gates} + +At stage $i$ the model receives $i$ latent tokens, one per hop, and is trained +with cross-entropy against a single node drawn uniformly from $F_{i+1}$; no +intermediate node is supervised. Since the prompt does not depend on which +frontier node is drawn, the Bayes-optimal predictor at hop $k$ is +$\mathrm{Unif}(F_k)$. + +We separate two decisions that a single-threshold curriculum conflates. The +promotion gate advances stage $i$ to $i+1$ once the promotion metric clears its +threshold on every hop up to $i+1$. The retention gate operates independently: +after each validation pass we evaluate the retention metric at every mastered hop +$k\le i$, and if any hop has fallen below threshold we return training to the +earliest such hop until it recovers, then resume at stage $i$. Repair does not +generate new graphs: it retrains on the same training set, reformatted as +stage-$k$ examples ($k$ latents, target from $F_{k+1}$), and leaves the +optimiser state untouched. The training objective is therefore unchanged, and +differences between arms are attributable to the gate alone. + +We compare three retention criteria of increasing strictness. Let $p$ be the +model's next-token distribution at hop $k$. \emph{Hop accuracy} is the fraction +of instances with $\arg\max_v p(v)\in F_k$; a model permanently committed to one +arm scores $1.0$ under it. \emph{Superposition} requires +$\mathrm{top}_{|F_k|}(p)=F_k$, but being a rank statistic it cannot separate a +true superposition from a commitment whose runner-up is the other arm: both +$p=(0.5,0.5)$ and $p=(0.98,0.02)$ over the two frontier nodes pass. + +The \emph{balance score} is the graded version of that test. Writing +$\mathrm{CE}_k=\frac{1}{|F_k|}\sum_{v\in F_k}(-\log p(v))$ for the +cross-entropy of $p$ against $\mathrm{Unif}(F_k)$---equivalently, the negative +log of the geometric mean of the frontier probabilities---we report +\[ + s_k \;=\; \min\!\bigl(1,\;\exp(\log|F_k|-\mathrm{CE}_k)\bigr). +\] +For the two-arm star, $|F_k|=2$. Under perfect equal superposition, +$p(A)=p(B)=\tfrac12$, one has $\mathrm{CE}_k=\log 2$ and therefore $s_k=1$. +That value of $\mathrm{CE}_k$ is the floor: no distribution can achieve a lower +cross-entropy against $\mathrm{Unif}(F_k)$. As either arm collapses, the +geometric mean collapses with it, $\mathrm{CE}_k$ rises, and $s_k$ falls toward +zero; for example $p=(0.98,0.02)$ yields $\mathrm{CE}_k\approx 1.97$ and +$s_k\approx 0.28$, which hop accuracy and superposition both score as correct. +At our threshold $s_k\ge 0.9$, the most unbalanced on-frontier split that still +passes is approximately $(0.72,0.28)$. +We do not use the entropy of $p$ over the full vocabulary; the quantity gated on +is this frontier-restricted cross-entropy, mapped to $[0,1]$ so that the same +threshold convention applies to all three criteria. + +\subsubsection{Experimental Setup} +\label{sec:bt-setup} + +\textbf{Model.} Unlike Sections~\ref{sec:graph-reasoning} and~\ref{sec:sudoku}, +which fine-tune pretrained Qwen models, we train a GPT-2 architecture from +scratch with two layers, eight heads and embedding dimension $768$. The +vocabulary has $128$ entries and node identifiers are literal tokens, so a node +identifier and its token index coincide, which makes the probe of +Section~\ref{sec:bt-probing} exact rather than approximate. Latent recurrence +follows Coconut. + +\textbf{Data.} We generate $14{,}000$ training and $256$ validation instances at +$L=10$ with disjoint seeds, drawing identifiers without replacement from a pool +of $47$ so that identifier ranges cannot serve as a shortcut. + +\textbf{Training.} AdamW with learning rate $10^{-4}$, weight decay $0.01$, batch +size $128$, gradient clipping $1.0$ and $200$ warmup steps, in full precision, +backpropagating through the entire latent chain without truncation, for $2000$ +epochs on one GPU. Both thresholds are $0.9$, so the comparison isolates the +choice of metric rather than its calibration, and all arms warm-start from a +shared stage-$0$ checkpoint. + +\textbf{Evaluation.} Every five epochs we evaluate all $256$ validation instances +at every hop, recording all three metrics regardless of which gates a run uses. +Two controls disable retention: one promotes on the current hop alone, the other +only when every mastered hop clears threshold, detecting regression without +repairing it. + +\subsubsection{Results} +\label{sec:bt-results} + +\begin{table}[t] +\centering +\small +\caption{Retention criteria at $L=10$. Hop accuracy, superposition and decoy +leakage are averaged over hops $1$--$10$ on the $256$ validation instances at the +final checkpoint; repairs count validation passes that triggered a return to an +earlier stage.} +\label{tab:bt-main} +\begin{tabular}{lcccccc} +\toprule +Retention criterion & Repairs & Time to & Val.\ acc.\ & Hop & Super- & Decoy \\ + & fired & stage $10$ & peak / final & acc. & position & leakage \\ +\midrule +Balance score $\ge 0.9$ & $93/400$ & $5824$\,s & $1.000$ / $0.988$ & $1.000$ & $\mathbf{0.999}$ & $0.000$ \\ +Superposition $\ge 0.9$ & $78/400$ & $4268$\,s & $1.000$ / $0.922$ & $1.000$ & $0.992$ & $0.000$ \\ +Hop accuracy $\ge 0.9$ & $52/400$ & $\mathbf{3896}$\,s & $0.836$ / $0.680$ & $0.996$ & $0.801$ & $0.002$ \\ +\midrule +none, current-stage promote & --- & $13359$\,s & $0.785$ / $0.684$ & $0.967$ & $0.152$ & $0.017$ \\ +none, retention-gated promote & --- & \emph{stalls at $5$} & $0.469$ / $0.113$ & $0.855$ & $0.071$ & $0.078$ \\ +\bottomrule +\end{tabular} +\end{table} + +\begin{figure}[t] +\centering +\includegraphics[width=0.48\textwidth]{figs/panelA_stagewise_learning.png} +\hfill +\includegraphics[width=0.48\textwidth]{figs/panelE_superposition.png} +\caption{Left: validation accuracy (solid) and curriculum stage (dotted) against +epoch. The control that detects regression but cannot repair it deadlocks at +stage $5$; the control promoting on the current hop alone finishes but needs +$3.4\times$ the wallclock of the fastest backtracking arm. Right: per-hop +superposition at the final checkpoints, which separates the same five arms by +more than an order of magnitude and collapses with depth wherever the retention +gate cannot observe it.} +\label{fig:bt-main} +\end{figure} + +All three backtracking arms complete the ten-stage curriculum, while the control +that detects regression without repairing it deadlocks permanently at stage $5$, +correctly refusing to promote but never recovering; since this control differs +from the backtracking arms only in its ability to act on what it detects, the +comparison isolates repair from detection and shows that detection alone is not +sufficient. The second control reaches stage $10$ but takes $13359$ seconds +against $3896$ for the hop-accuracy arm, because each new stage must relearn hops +it has silently broken, so targeted repair is cheaper than omitting it. The gate +is inexpensive, firing on $13$ to $23$ percent of validation passes and +concentrating on shallow hops, with $43$ percent of balance-score repairs +returning to the first. Strictness trades wallclock for quality monotonically: +repairs rise from $52$ to $78$ to $93$ and wallclock from $3896$ to $5824$ +seconds, while final accuracy rises from $0.680$ to $0.922$ to $0.988$. + +\subsubsection{What the Latent Tokens Encode} +\label{sec:bt-probing} + +Because node identifiers and token indices coincide, applying the language +modelling head at a latent position yields a distribution over graph nodes with +no learned probe. We decode every latent slot of every validation instance and +classify the prediction by its true role: its reachable distance from the root, +membership in the decoy component, or absence from the graph. + +The latent chain has discovered a positional encoding of breadth-first depth. For +every arm, exact-depth accuracy agrees with hop accuracy to three decimals, +meaning a latent that decodes to a reachable node lands at exactly its own slot's +depth and never an adjacent one; the slot-by-role confusion matrices are +correspondingly diagonal and no phase drift accumulates. This is the mechanistic +counterpart of the nested-containment result of Section~\ref{sec:sudoku}, +established over individual latent states rather than output sets. + +Per-hop accuracy, the quantity a retention gate would most naturally monitor, is +nonetheless blind to the difference the criterion makes. It spans only $0.855$ to +$1.000$ across the five arms, a range within which one would judge them +equivalent, while mean superposition on the same checkpoints spans $0.071$ to +$0.999$ and final task accuracy spans $0.113$ to $0.988$. The control promoting +on the current hop attains $0.967$ per-hop accuracy while representing both +successors on fifteen percent of its latents, falling to $0.031$ by the fifth +hop; it completes the curriculum by committing to an arm and being right half the +time, which per-hop accuracy cannot detect because a committed guess still lands +on the frontier. As Figure~\ref{fig:bt-main} +shows, the arms differ in where collapse begins rather than whether it occurs: +the hop-accuracy arm holds $1.000$ at the first hop and decays to $0.391$ at the +tenth while its own gate reads $0.996$ throughout and never fires, whereas the +balance-score arm stays above $0.996$ at all ten hops. Decoy leakage follows the +same ordering, zero under the two stricter criteria and $0.152$ by the tenth hop +for the deadlocked control. + +These results are established at $L=10$; at $L=15$ the recipe completes the +curriculum only partially and at $L=20$ it does not pass the early stages within +our budget. The binding constraint appears to be sample complexity rather than +capacity, since enlarging the training set moves the stall depth whereas doubling +the layer count does not. + +\end{document} diff --git a/notebook.ipynb b/notebook.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..74e4b31479085268d6bba05b6df5cd0a3af88e25 --- /dev/null +++ b/notebook.ipynb @@ -0,0 +1,1020 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This interactive notebook illustrates how the Coconut model leverages superposition states for reasoning. We visualize the attention patterns from the first and second transformer layers, and compute the inner products between continuous thoughts and node embeddings to reveal how information propagates and accumulates during inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# preparation\n", + "import argparse\n", + "from transformers import AutoModelForCausalLM, AutoConfig\n", + "import torch\n", + "from coconut import Coconut\n", + "from stokenizer import STokenizer\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "import random \n", + "import numpy as np\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from huggingface_hub import hf_hub_download\n", + "\n", + "print(\"loading weights\")\n", + "checkpoint_path = hf_hub_download(\n", + " repo_id=\"Shibo-UCSD/coconut-theory\",\n", + " filename=\"checkpoint_300\"\n", + ")\n", + "# note that this checkpoint is only an example to visualize the reasoning pattern.\n", + "# it doesn't precisely reproduce the reported accuracy on ProsQA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "saved_weights = torch.load(\n", + " checkpoint_path, \n", + " map_location=torch.device(\"cuda:0\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "evaluating\n" + ] + }, + { + "data": { + "text/plain": [ + "GPT2LMHeadModel(\n", + " (transformer): GPT2Model(\n", + " (wte): Embedding(40, 768)\n", + " (wpe): Embedding(1024, 768)\n", + " (drop): Dropout(p=0.1, inplace=False)\n", + " (h): ModuleList(\n", + " (0-1): 2 x GPT2Block(\n", + " (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (attn): GPT2Attention(\n", + " (c_attn): Conv1D(nf=2304, nx=768)\n", + " (c_proj): Conv1D(nf=768, nx=768)\n", + " (attn_dropout): Dropout(p=0.1, inplace=False)\n", + " (resid_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): GPT2MLP(\n", + " (c_fc): Conv1D(nf=3072, nx=768)\n", + " (c_proj): Conv1D(nf=768, nx=3072)\n", + " (act): NewGELUActivation()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (lm_head): Linear(in_features=768, out_features=40, bias=False)\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# loading the model\n", + "\n", + "tokenizer = STokenizer()\n", + "latent_id = tokenizer.convert_tokens_to_ids(\"<|latent|>\")\n", + "\n", + "model = AutoModelForCausalLM.from_config(\n", + " AutoConfig.from_pretrained(\"configs/symbol-2layer-8head-768dim.json\")\n", + ")\n", + "start_latent_id = tokenizer.convert_tokens_to_ids(\"<|start-latent|>\")\n", + "end_latent_id = tokenizer.convert_tokens_to_ids(\"<|end-latent|>\")\n", + "model = Coconut(model, latent_id, start_latent_id, end_latent_id, tokenizer.eos_token_id)\n", + "print(model.load_state_dict(saved_weights, strict=False))\n", + "print(\"evaluating\")\n", + "model.eval()\n", + "model.base_causallm.to(\"cuda:0\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First Layer Attention" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "According to our theoretical construction, the most important function of LAYER 1 attention heads is to copy the source and target node tokens of an edge onto the corresponding edge\n", + "token ⟨e⟩. The following code block presents a representative attention map, confirming that the model has instantiated this copying mechanism in practice." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.53.0. You should pass an instance of `Cache` instead, e.g. `past_key_values=DynamicCache.from_legacy_cache(past_key_values)`.\n", + "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to eager attention. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.\n" + ] + } + ], + "source": [ + "inputs = \" 5 15 | 11 16 | 1 3 | 14 17 | 0 14 | 1 8 | 0 9 | 0 5 | 15 16 | 3 4 | 19 20 | 3 7 | 7 20 | 9 17 | 0 15 | 9 10 | 14 16 | 10 12 | 2 6 | 4 8 | 5 9 | 8 19 | 7 13 | 7 11 | 7 8 | 5 21 | 1 12 | 16 22 | 3 8 | 0 10 | 5 18 | 13 22 | 5 10 | 7 22 [Q] 20 18 [R] 1 <|latent|>\"\n", + "input_ids = tokenizer.encode(inputs, add_special_tokens=False)\n", + "input_ids = torch.tensor(input_ids).unsqueeze(0).cuda(0)\n", + "attention_mask = torch.ones_like(input_ids).cuda(0)\n", + "labels = torch.full_like(input_ids, -100).cuda(0)\n", + "position_ids = torch.arange(input_ids.shape[1]).unsqueeze(0).cuda(0)\n", + "outputs = model(input_ids, attention_mask, labels, position_ids)\n", + "\n", + "latent_indices = (\n", + " input_ids == model.latent_token_id\n", + ").nonzero() # (num_latent_tokens_in_the_batch, 2)\n", + "\n", + "latent_lists = [\n", + " [idx[1].item() for idx in latent_indices if idx[0] == i]\n", + " for i in range(input_ids.shape[0])\n", + "] # bs, num_latent_tokens_in_the_instance (difference across the batch)\n", + "\n", + "max_n_latents = max([len(l) for l in latent_lists])\n", + "\n", + "next_compute_range = (0, input_ids.shape[1])\n", + "inputs_embeds = model.embedding(input_ids)\n", + "logits = []\n", + "\n", + "if max_n_latents > 0:\n", + " next_compute_range = (0, latent_indices[:, 1].min().item())\n", + " # before the earliest latent token position\n", + "\n", + "kv_cache = None\n", + "\n", + "for pass_idx in range(max_n_latents):\n", + "\n", + " if kv_cache == None:\n", + " # first forward pass\n", + " outputs = model.base_causallm(\n", + " inputs_embeds=inputs_embeds[\n", + " :, next_compute_range[0] : next_compute_range[1], :\n", + " ],\n", + " attention_mask=attention_mask[\n", + " :, next_compute_range[0] : next_compute_range[1]\n", + " ],\n", + " position_ids=position_ids[\n", + " :, next_compute_range[0] : next_compute_range[1]\n", + " ],\n", + " output_hidden_states=True,\n", + " output_attentions=True,\n", + " )\n", + " hidden_states_offset = 0\n", + "\n", + " else:\n", + " # extract kv cache to reuse\n", + " past_key_values = [\n", + " (\n", + " k[:, :, : next_compute_range[0], :],\n", + " v[:, :, : next_compute_range[0], :],\n", + " )\n", + " for k, v in kv_cache\n", + " ]\n", + "\n", + " outputs = model.base_causallm(\n", + " inputs_embeds=inputs_embeds[\n", + " :, next_compute_range[0] : next_compute_range[1], :\n", + " ],\n", + " attention_mask=attention_mask[:, : next_compute_range[1]],\n", + " position_ids=position_ids[\n", + " :, next_compute_range[0] : next_compute_range[1]\n", + " ],\n", + " past_key_values=past_key_values,\n", + " output_hidden_states=True,\n", + " output_attentions=True,\n", + " )\n", + "\n", + " hidden_states_offset = next_compute_range[0]\n", + " # when we use kv_cache for the first k tokens\n", + " # in `outputs.hidden_states`, [0, k) will be skipped\n", + " # so we need to keep this offset to correctly use the last hidden states\n", + "\n", + " logits.append(outputs.logits)\n", + "\n", + " next_compute_range = (\n", + " next_compute_range[1],\n", + " (\n", + " input_ids.shape[1]\n", + " if pass_idx + 1 >= max_n_latents\n", + " else next_compute_range[1] + 1\n", + " ),\n", + " )\n", + "\n", + " hidden_states = outputs.hidden_states[\n", + " -1\n", + " ] # Get the last layer hidden states\n", + " kv_cache = outputs.past_key_values\n", + "\n", + " # feedback the continuous thoughts to the input_embeds\\\n", + " # first decide the positions to feedback\n", + " filling_indices = [\n", + " (instance_idx, mask_list[pass_idx])\n", + " for instance_idx, mask_list in enumerate(latent_lists)\n", + " if len(mask_list) > pass_idx\n", + " ]\n", + "\n", + " # to avoid in-place operations\n", + " # break down inputs_embeds (bs, len, hidden_size) into a list of list of 1-d tensors\n", + " tensor_list = [\n", + " [\n", + " inputs_embeds[batch_idx, pos, :]\n", + " for pos in range(inputs_embeds.shape[1])\n", + " ]\n", + " for batch_idx in range(inputs_embeds.shape[0])\n", + " ]\n", + "\n", + " # replace some of them with continuous thoughts\n", + " for idx_pair in filling_indices:\n", + " batch_idx, token_idx = idx_pair\n", + "\n", + " # replace it with the preceding last hidden states\n", + " tensor_list[batch_idx][token_idx] = hidden_states[\n", + " batch_idx, token_idx - 1 - hidden_states_offset, :\n", + " ]\n", + "\n", + " # assemble the new inputs_embeds\n", + " inputs_embeds = torch.stack(\n", + " [\n", + " torch.stack(tensor_list[batch_idx])\n", + " for batch_idx in range(inputs_embeds.shape[0])\n", + " ]\n", + " )\n", + "# final pass\n", + "final_outputs = model.base_causallm(\n", + " inputs_embeds=inputs_embeds[\n", + " :, next_compute_range[0] : next_compute_range[1], :\n", + " ],\n", + " attention_mask=attention_mask[:, : next_compute_range[1]],\n", + " position_ids=position_ids[:, next_compute_range[0] : next_compute_range[1]],\n", + " past_key_values=(\n", + " [\n", + " (\n", + " k[:, :, : next_compute_range[0], :],\n", + " v[:, :, : next_compute_range[0], :],\n", + " )\n", + " for k, v in kv_cache\n", + " ]\n", + " if kv_cache\n", + " else None\n", + " ),\n", + " output_hidden_states=True,\n", + " output_attentions=True,\n", + ")\n", + "\n", + "logits.append(final_outputs.logits)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([107, 107])\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print(outputs.attentions[1][0][head_idx][-1, :])\n", + "\n", + "# draw an attention map\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Get the attention map for the last layer and head\n", + "\n", + "first_layer_attentions = outputs.attentions[0][0][:, :, :].sum(dim=0)\n", + "# layer, batch, head, source_seq_len, target_seq_len\n", + "\n", + "\n", + "print(first_layer_attentions.shape)\n", + "# 107 * 107\n", + "\n", + "# Create a heatmap of the attention weights\n", + "n_show = 16\n", + "show_attention = first_layer_attentions[:n_show, :n_show]\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "# show all the x and y labels\n", + "# x label rotate 90 degrees\n", + "\n", + "inputs_ = inputs.replace(\"|\", \"\").replace(\"\", \"\")\n", + "\n", + "plt.xticks(range(n_show), inputs_.split(\" \")[:n_show])\n", + "plt.xticks(rotation=0)\n", + "\n", + "plt.yticks(range(n_show), inputs_.split(\" \")[:n_show])\n", + "# add labels for x and y\n", + "plt.xlabel('Input Tokens (Key)')\n", + "plt.ylabel('Input Tokens (Query)')\n", + "\n", + "\n", + "# rotate the x and y labels\n", + "plt.xticks(rotation=90)\n", + "plt.yticks(rotation=0)\n", + "\n", + "plt.imshow(show_attention.detach().cpu().numpy(), cmap='viridis', aspect='auto')\n", + "plt.colorbar(label='Attention Weight')\n", + "\n", + "# tight\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Second Layer Attention" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Layer 2** is responsible for node expansion: at each step, the continuous thought attends to all outgoing edges from nodes that are currently reachable. To quantify this behavior, we compute the aggregated attention score received by each edge token triplet $(s, t, \\langle e \\rangle)$ across all heads when generating the $i$-th continuous thought. We categorize edges into four types: \n", + "1. **Reachable** – edges whose source node is in the reachable set at step $i$; \n", + "2. **Not Reachable** – source node is not yet reachable; \n", + "3. **Frontier** – a subset of reachable edges whose source nodes lie on the current search frontier, i.e., exactly $i$ steps from the root; \n", + "4. **Optimal** – a subset of frontier edges that lie along the optimal reasoning path.\n", + "\n", + "We report group-wise means averaged over the test set. The model strongly concentrates its attention on **Reachable** edges, aligning with our theoretical construction. Interestingly, it also exhibits a bias toward the **Frontier** subset, possibly because the training objective encourages predicting frontier nodes at each step, while attention to previously explored nodes naturally decays. Furthermore, **Optimal** edges tend to receive higher attention scores, likely due to supervision from multi-stage training based on chain-of-thought (CoT) solutions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "def get_attn(question):\n", + " input_ids = tokenizer.encode(question, add_special_tokens=False)\n", + " input_ids = torch.tensor(input_ids).unsqueeze(0).to(model.base_causallm.device)\n", + " attention_mask = torch.ones_like(input_ids).to(model.base_causallm.device)\n", + " labels = torch.full_like(input_ids, -100).to(model.base_causallm.device)\n", + " position_ids = torch.arange(input_ids.shape[1]).unsqueeze(0).to(model.base_causallm.device)\n", + " outputs = model(input_ids, attention_mask, labels, position_ids)\n", + "\n", + " latent_indices = (\n", + " input_ids == model.latent_token_id\n", + " ).nonzero() # (num_latent_tokens_in_the_batch, 2)\n", + "\n", + " latent_lists = [\n", + " [idx[1].item() for idx in latent_indices if idx[0] == i]\n", + " for i in range(input_ids.shape[0])\n", + " ] # bs, num_latent_tokens_in_the_instance (difference across the batch)\n", + "\n", + " max_n_latents = max([len(l) for l in latent_lists])\n", + "\n", + " next_compute_range = (0, input_ids.shape[1])\n", + " inputs_embeds = model.embedding(input_ids)\n", + "\n", + " if max_n_latents > 0:\n", + " next_compute_range = (0, latent_indices[:, 1].min().item())\n", + " # before the earliest latent token position\n", + "\n", + " kv_cache = None\n", + "\n", + " for pass_idx in range(max_n_latents):\n", + "\n", + " if kv_cache == None:\n", + " # first forward pass\n", + " outputs = model.base_causallm(\n", + " inputs_embeds=inputs_embeds[\n", + " :, next_compute_range[0] : next_compute_range[1], :\n", + " ],\n", + " attention_mask=attention_mask[\n", + " :, next_compute_range[0] : next_compute_range[1]\n", + " ],\n", + " position_ids=position_ids[\n", + " :, next_compute_range[0] : next_compute_range[1]\n", + " ],\n", + " output_hidden_states=True,\n", + " output_attentions=True,\n", + " )\n", + " hidden_states_offset = 0\n", + "\n", + " else:\n", + " # extract kv cache to reuse\n", + " past_key_values = [\n", + " (\n", + " k[:, :, : next_compute_range[0], :],\n", + " v[:, :, : next_compute_range[0], :],\n", + " )\n", + " for k, v in kv_cache\n", + " ]\n", + "\n", + " outputs = model.base_causallm(\n", + " inputs_embeds=inputs_embeds[\n", + " :, next_compute_range[0] : next_compute_range[1], :\n", + " ],\n", + " attention_mask=attention_mask[:, : next_compute_range[1]],\n", + " position_ids=position_ids[\n", + " :, next_compute_range[0] : next_compute_range[1]\n", + " ],\n", + " past_key_values=past_key_values,\n", + " output_hidden_states=True,\n", + " output_attentions=True,\n", + " )\n", + "\n", + " hidden_states_offset = next_compute_range[0]\n", + " # when we use kv_cache for the first k tokens\n", + " # in `outputs.hidden_states`, [0, k) will be skipped\n", + " # so we need to keep this offset to correctly use the last hidden states\n", + "\n", + " # logits.append(outputs.logits)\n", + "\n", + " next_compute_range = (\n", + " next_compute_range[1],\n", + " (\n", + " input_ids.shape[1]\n", + " if pass_idx + 1 >= max_n_latents\n", + " else next_compute_range[1] + 1\n", + " ),\n", + " )\n", + "\n", + " hidden_states = outputs.hidden_states[\n", + " -1\n", + " ] # Get the last layer hidden states\n", + " kv_cache = outputs.past_key_values\n", + "\n", + " # feedback the continuous thoughts to the input_embeds\n", + "\n", + " # first decide the positions to feedback\n", + " filling_indices = [\n", + " (instance_idx, mask_list[pass_idx])\n", + " for instance_idx, mask_list in enumerate(latent_lists)\n", + " if len(mask_list) > pass_idx\n", + " ]\n", + "\n", + " # to avoid in-place operations\n", + " # break down inputs_embeds (bs, len, hidden_size) into a list of list of 1-d tensors\n", + " tensor_list = [\n", + " [\n", + " inputs_embeds[batch_idx, pos, :]\n", + " for pos in range(inputs_embeds.shape[1])\n", + " ]\n", + " for batch_idx in range(inputs_embeds.shape[0])\n", + " ]\n", + "\n", + " # replace some of them with continuous thoughts\n", + " for idx_pair in filling_indices:\n", + " batch_idx, token_idx = idx_pair\n", + "\n", + " # replace it with the preceding last hidden states\n", + " tensor_list[batch_idx][token_idx] = hidden_states[\n", + " batch_idx, token_idx - 1 - hidden_states_offset, :\n", + " ]\n", + "\n", + " # assemble the new inputs_embeds\n", + " inputs_embeds = torch.stack(\n", + " [\n", + " torch.stack(tensor_list[batch_idx])\n", + " for batch_idx in range(inputs_embeds.shape[0])\n", + " ]\n", + " )\n", + " # final pass\n", + " final_outputs = model.base_causallm(\n", + " inputs_embeds=inputs_embeds[\n", + " :, next_compute_range[0] : next_compute_range[1], :\n", + " ],\n", + " attention_mask=attention_mask[:, : next_compute_range[1]],\n", + " position_ids=position_ids[:, next_compute_range[0] : next_compute_range[1]],\n", + " past_key_values=(\n", + " [\n", + " (\n", + " k[:, :, : next_compute_range[0], :],\n", + " v[:, :, : next_compute_range[0], :],\n", + " )\n", + " for k, v in kv_cache\n", + " ]\n", + " if kv_cache\n", + " else None\n", + " ),\n", + " output_hidden_states=True,\n", + " output_attentions=True,\n", + " )\n", + "\n", + " # logits.append(final_outputs.logits)\n", + " last_layer_attentions = final_outputs.attentions[1][0][:, -1, :]\n", + " return last_layer_attentions" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = json.load(open(\"data/prosqa_test_graph_4_coconut.json\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:02<00:00, 187.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "frontier 1.8065\n", + "optimal 2.1848\n", + "invalid 0.0732\n", + "within_k 1.8065\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:04<00:00, 96.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "frontier 0.7455\n", + "optimal 1.2767\n", + "invalid 0.071\n", + "within_k 0.5616\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:06<00:00, 65.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "frontier 0.5061\n", + "optimal 1.2628\n", + "invalid 0.118\n", + "within_k 0.3161\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:04<00:00, 96.83it/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "frontier 0.4542\n", + "optimal 1.6539\n", + "invalid 0.1529\n", + "within_k 0.2476\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "for n_step in range(0, 4):\n", + "\n", + " att_group_frontier = []\n", + " att_group_within_k = []\n", + " att_group_optimal = []\n", + " att_group_invalid = []\n", + "\n", + " # show progress bar\n", + " for data in tqdm(dataset):\n", + " \n", + " # data['neighbor_k'][str(n_step + 1)]\n", + " \n", + " if str(n_step + 1) not in data['neighbor_k']:\n", + " continue\n", + " \n", + " symbol_to_idx = {}\n", + " for i, s in enumerate(data['idx_to_symbol']):\n", + " symbol_to_idx[s] = i\n", + " \n", + " random.shuffle(data['edges'])\n", + "\n", + " question = \" \" + \"|\".join([f\" {e[0]} {e[1]} \" for e in data['edges']]).strip() + \\\n", + " \" [Q] \"\n", + " if random.random() < 0.5:\n", + " question += str(data['target']) + \" \" + str(data['neg_target'])\n", + " else:\n", + " question += str(data['neg_target']) + \" \" + str(data['target'])\n", + "\n", + " question += \" [R] \" + str(data['root']) + \" <|latent|>\" * n_step\n", + "\n", + " last_layer_attentions = get_attn(question)\n", + "\n", + " \n", + " # sum the attention weights of all the heads\n", + " attn_sum = last_layer_attentions.sum(dim=0)\n", + " # plot the sum\n", + " # count the number of heads that have attention weights greater than 0.5\n", + " \n", + " current_node_set = [data['root']]\n", + " \n", + " reachable_node_set = set(current_node_set)\n", + " \n", + " for _ in range(n_step):\n", + " next_node_set = []\n", + " for node in current_node_set:\n", + " for edge in data['edges']:\n", + " if edge[0] == node:\n", + " next_node_set.append(edge[1])\n", + " current_node_set = next_node_set\n", + " reachable_node_set.update(current_node_set)\n", + " \n", + " # find index of (root, neighbor) in the question\n", + " for i in range(1, len(question.split(\" \")), 3):\n", + " if question.split(\" \")[i + 2] != \"|\":\n", + " break\n", + " \n", + " if int(question.split(\" \")[i]) in reachable_node_set:\n", + " \n", + " att_group_within_k.append(attn_sum[i:i+3].sum().item())\n", + " \n", + " if int(question.split(\" \")[i]) in current_node_set:\n", + " att_group_frontier.append(attn_sum[i:i+3].sum().item())\n", + " if int(question.split(\" \")[i + 1]) in data['neighbor_k'][str(n_step + 1)]:\n", + " att_group_optimal.append(attn_sum[i:i+3].sum().item())\n", + " \n", + " else:\n", + " # print(\"invalid\", i, question.split(\" \")[i: i+3])\n", + " att_group_invalid.append(attn_sum[i:i+3].sum().item())\n", + "\n", + " # break\n", + " \n", + " # draw a dist plot of att_group_valid and att_group_invalid\n", + " # print(len(att_group_frontier), len(att_group_optimal), len(att_group_invalid))\n", + " print(n_step)\n", + " print(\"frontier\", round(sum(att_group_frontier) / len(att_group_frontier), 4))\n", + " print(\"optimal\", round(sum(att_group_optimal) / len(att_group_optimal), 4))\n", + " print(\"invalid\", round(sum(att_group_invalid) / len(att_group_invalid), 4))\n", + " print(\"within_k\", round(sum(att_group_within_k) / len(att_group_within_k), 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis of the continuous thoughts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Representation of Continuous Thoughts**\n", + "\n", + "To verify that continuous thoughts serve as superposition states for the search process, we compute the inner product between the continuous thought at step $i$, denoted $[t_i]$, and each node embedding $u_v$. As with edge classification, we categorize nodes into four groups: \n", + "1. **Reachable** – nodes within the reachable set at step $i$; \n", + "2. **Not Reachable** – nodes not yet reachable; \n", + "3. **Frontier** – nodes exactly $i$ steps from the root (the current search frontier); \n", + "4. **Optimal** – nodes on the optimal reasoning path.\n", + "\n", + "The following code block shows the similarity distributions segmented by reasoning step $i$. As predicted, nodes within $i$ hops exhibit significantly higher similarity to $[t_i]$ than more distant nodes. Notably, **Frontier** nodes are even more similar to the thought vector than other reachable nodes, indicating that the superposition emphasizes candidate expansion fronts. **Optimal** nodes are closer still, likely due to training supervision consistently highlighting optimal paths.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# define the function to compute the continuous thoughts\n", + "\n", + "def get_continuous_thoughts(question):\n", + " input_ids = tokenizer.encode(question, add_special_tokens=False)\n", + " input_ids = torch.tensor(input_ids).unsqueeze(0).to(model.base_causallm.device)\n", + " attention_mask = torch.ones_like(input_ids).to(model.base_causallm.device)\n", + " labels = torch.full_like(input_ids, -100).to(model.base_causallm.device)\n", + " position_ids = torch.arange(input_ids.shape[1]).unsqueeze(0).to(model.base_causallm.device)\n", + " outputs = model(input_ids, attention_mask, labels, position_ids)\n", + " return outputs.inputs_embeds" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# load the dataset\n", + "dataset = json.load(open(\"data/prosqa_test_graph_4_coconut.json\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 419/419 [00:02<00:00, 173.98it/s]\n", + "100%|██████████| 419/419 [00:03<00:00, 127.93it/s]\n", + "100%|██████████| 419/419 [00:04<00:00, 101.10it/s]\n", + "100%|██████████| 419/419 [00:02<00:00, 159.63it/s]\n" + ] + } + ], + "source": [ + "stats = {}\n", + "\n", + "for n_step in range(0, 4):\n", + "\n", + " inner_prod_frontier = []\n", + " inner_prod_within_k = []\n", + " inner_prod_invalid = []\n", + " inner_prod_optimal = []\n", + "\n", + " # Get token embeddings for comparison\n", + " token_embeddings = model.embedding.weight\n", + "\n", + " for data in tqdm(dataset):\n", + " if str(n_step + 1) not in data['neighbor_k']:\n", + " continue\n", + " \n", + " # Construct question string\n", + " question = \" \" + \"|\".join([f\" {e[0]} {e[1]} \" for e in data['edges']]).strip() + \\\n", + " \" [Q] \"\n", + " if random.random() < 0.5:\n", + " question += str(data['target']) + \" \" + str(data['neg_target'])\n", + " else:\n", + " question += str(data['neg_target']) + \" \" + str(data['target'])\n", + " question += \" [R] \" + str(data['root']) + \" <|latent|>\" * (n_step + 1)\n", + " # the last input embedding is the previous latent continuous thought\n", + "\n", + " # Get continuous thoughts embeddings\n", + " continuous_embeds = get_continuous_thoughts(question)\n", + " \n", + " # Find positions of latent tokens\n", + " input_ids = tokenizer.encode(question, add_special_tokens=False)\n", + " latent_positions = [i for i, id in enumerate(input_ids) if id == latent_id]\n", + " \n", + " if len(latent_positions) == 0:\n", + " continue\n", + " \n", + " # Get the last latent token's continuous thought\n", + " last_latent_pos = latent_positions[-1]\n", + " continuous_thought = continuous_embeds[0, last_latent_pos, :]\n", + " \n", + " # Compute inner products with all token embeddings\n", + " inner_products = torch.matmul(continuous_thought, token_embeddings.t().to(continuous_thought.device))\n", + " \n", + " # Track current nodes for step n\n", + " current_node_set = [data['root']]\n", + " previous_node_set = []\n", + " for _ in range(n_step + 1):\n", + " previous_node_set += current_node_set\n", + " next_node_set = []\n", + " for node in current_node_set:\n", + " for edge in data['edges']:\n", + " if edge[0] == node:\n", + " next_node_set.append(edge[1])\n", + " current_node_set = next_node_set\n", + " \n", + " # Classify nodes and compute average inner products\n", + " all_nodes = set()\n", + " for edge in data['edges']:\n", + " all_nodes.add(edge[0])\n", + " all_nodes.add(edge[1])\n", + " \n", + " optimal_node_set = set(data['neighbor_k'][str(n_step + 1)])\n", + " \n", + " for node in all_nodes:\n", + " node_token_id = tokenizer.encode(str(node), add_special_tokens=False)[0]\n", + " inner_prod = inner_products[node_token_id].item()\n", + " \n", + " if node in current_node_set + previous_node_set:\n", + " inner_prod_within_k.append(inner_prod)\n", + " if node in current_node_set:\n", + " inner_prod_frontier.append(inner_prod)\n", + " if node in optimal_node_set:\n", + " inner_prod_optimal.append(inner_prod)\n", + " else:\n", + " inner_prod_invalid.append(inner_prod)\n", + "\n", + " # save the inner products\n", + " stats[n_step] = {\n", + " \"step\": n_step,\n", + " \"inner_prod_invalid\": inner_prod_invalid,\n", + " \"inner_prod_within_k\": inner_prod_within_k,\n", + " \"inner_prod_frontier\": inner_prod_frontier,\n", + " \"inner_prod_optimal\": inner_prod_optimal\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a 2x4 subplot layout\n", + "fig, axes = plt.subplots(1, 4, figsize=(14, 3), sharey=False)\n", + "\n", + "# First row: Original model\n", + "for step in range(4):\n", + " # Load data for each step\n", + " \n", + " data = stats[step]\n", + " # Extract data\n", + " inner_prod_invalid = data['inner_prod_invalid']\n", + " inner_prod_within_k = data['inner_prod_within_k']\n", + " inner_prod_frontier = data['inner_prod_frontier']\n", + " inner_prod_optimal = data['inner_prod_optimal']\n", + " \n", + " # Calculate means\n", + " mean_invalid = np.mean(inner_prod_invalid)\n", + " mean_within_k = np.mean(inner_prod_within_k)\n", + " mean_frontier = np.mean(inner_prod_frontier)\n", + " mean_optimal = np.mean(inner_prod_optimal)\n", + " \n", + " # Define common bins for this subplot\n", + " bins = np.linspace(\n", + " min(min(inner_prod_invalid), min(inner_prod_within_k)),\n", + " max(max(inner_prod_invalid), max(inner_prod_within_k)),\n", + " 30\n", + " )\n", + " \n", + " # Plot on current subplot\n", + " ax = axes[step]\n", + " \n", + " # ax.set_xlabel('Inner Product Value')\n", + " \n", + " ax.hist(inner_prod_invalid, bins=bins, alpha=0.4, \n", + " label=f'Not Reachable ({mean_invalid:.2f})',\n", + " color='C0', edgecolor='C0', histtype='stepfilled', linewidth=0)\n", + " \n", + " ax.hist(inner_prod_within_k, bins=bins, alpha=0.3, \n", + " label=f'Reachable ({mean_within_k:.2f})',\n", + " color='C1', edgecolor='C1', histtype='stepfilled', linewidth=0)\n", + " \n", + " ax.hist(inner_prod_frontier, bins=bins, alpha=0.3, \n", + " label=f'Frontier ({mean_frontier:.2f})',\n", + " color='C1', histtype='bar',\n", + " hatch='...', edgecolor='C1', linewidth=0)\n", + " \n", + " ax.hist(inner_prod_optimal, bins=bins, alpha=0.5, \n", + " label=f'Optimal ({mean_optimal:.2f})',\n", + " color='C1', histtype='bar',\n", + " hatch='...', edgecolor='C1', linewidth=0)\n", + " \n", + " ax.set_title(f'Continuous thought {step + 1}')\n", + " ax.grid(alpha=0.3)\n", + " \n", + " # Only add x-label for bottom row\n", + " \n", + " ax.set_title(f'Continuous thought {step + 1}')\n", + " \n", + " # Show legend for all subplots with smaller font size\n", + " \n", + " from matplotlib.patches import Patch\n", + " legend_elements = [\n", + " Patch(color='C0', alpha=0.4, label=f'Not Reachable ({mean_invalid:.2f})'),\n", + " Patch(color='C1', alpha=0.3, label=f'Reachable ({mean_within_k:.2f})'),\n", + " Patch(color='C1', alpha=0.4, hatch='...', label=f'Frontier ({mean_frontier:.2f})'),\n", + " Patch(color='C1', alpha=0.7, hatch='...', label=f'Optimal ({mean_optimal:.2f})') # Higher alpha for optimal\n", + " ]\n", + " ax.legend(handles=legend_elements, fontsize='small', loc='upper right')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Together, these findings confirm that trained models implement a soft, parallel search mechanism through superpositional reasoning: **Layer 1** establishes the query context, **Layer 2** expands the search frontier, and the latent vectors encode reachable state sets in a continuous, distributed form—realizing the theoretical construction in the paper." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/overleaf_latent_backtracking/README.txt b/overleaf_latent_backtracking/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5bc802757be15265ccfe6b3251c3d4600efd483 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+version https://git-lfs.github.com/spec/v1 +oid sha256:00da98ebcf834214b6b059c9de8cd18ead55892f1c0e98cd8a6b459ed1ec1dc0 +size 102783 diff --git a/overleaf_latent_backtracking/main.tex b/overleaf_latent_backtracking/main.tex new file mode 100644 index 0000000000000000000000000000000000000000..731f2207f46a468b6b40f47d9275481f61b452db --- /dev/null +++ b/overleaf_latent_backtracking/main.tex @@ -0,0 +1,395 @@ +\documentclass[11pt]{article} + +\usepackage[margin=1in]{geometry} +\usepackage{amsmath,amssymb,booktabs,graphicx,hyperref,xcolor} +\usepackage{enumitem} +\setlist{leftmargin=1.5em,itemsep=0.25em,topsep=0.4em} + +\title{Latent Backtracking on Two-Arm Star Reachability} +\author{} +\date{} + +\begin{document} +\maketitle + +\section{Task} +\label{sec:task} + +Two-arm star reachability at depth $L$. Each instance has two disconnected +components: +\begin{itemize} + \item \textbf{Positive arm:} root $r$ with two chains of length $L$. + \item \textbf{Decoy arm:} isomorphic copy with disjoint node IDs. +\end{itemize} +The input is the shuffled edge list of both components, the root $r$, and two +candidate leaves (one from each component). The target is the reachable leaf. + +At hop $k$, the reachable frontier is +\[ + F_k \;=\; \{v : \mathrm{dist}(r,v)=k\}, +\] +with $|F_k|=2$ by construction (one node per arm). + +At $L=10$: $42$ nodes, $40$ edges, node IDs drawn from a pool of $47$. +Train / validation: $14{,}000$ / $256$ instances, disjoint seeds. + +\paragraph{Prompt format.} +\begin{verbatim} + u v | u v | ... [Q] t t' [R] r +\end{verbatim} +Edges of both components are shuffled. $\{t,t'\}$ is a random ordering of the +reachable leaf and its decoy counterpart. Each instance also stores +\texttt{neighbor\_k}: $k\mapsto F_k$, used only for evaluation and probing. + +\section{Model} +\label{sec:model} + +GPT-2 architecture trained from scratch: +\begin{itemize} + \item $2$ layers, $8$ heads, $d_{\mathrm{model}}=768$ + \item vocabulary size $128$ (node id $=$ token id) + \item $3$ special tokens: \texttt{<|start-latent|>}, \texttt{<|latent|>}, + \texttt{<|end-latent|>} +\end{itemize} +At curriculum stage $i$, the model receives $i$ latent tokens ($c=1$ per hop) +after the root. The training loss is cross-entropy on a single node sampled +uniformly from $F_{i+1}$. Intermediate latents are never supervised. + +\subsection{Latent propagation (exact Coconut forward)} +\label{sec:latent-prop} + +Write the token sequence as +\[ + x \;=\; + \bigl(\underbrace{x_1,\ldots,x_n}_{\text{prompt: edges, [Q], [R], root}},\; + \underbrace{z_1,\ldots,z_k}_{\texttt{<|latent|>}\ \text{slots}}\bigr). +\] +Let $t_j$ be the position of slot $z_j$ (so $t_1$ is immediately after the root). +Coconut does \emph{not} run one full-sequence forward. It runs $k{+}1$ passes with +a KV cache, matching \texttt{coconut.py}: + +\begin{enumerate}[leftmargin=1.6em] + \item \textbf{Embed.} + $E \leftarrow \mathrm{Embed}(x)$ (token embedding of every ID, including + the discrete \texttt{<|latent|>} placeholders). + \item \textbf{Pass $0$ (prefix).} + Run the transformer on positions $[0,\,t_1)$ (everything up to but not + including the first latent). Let $h_{t_1-1}$ be the last-layer hidden + state at the root. Store the KV cache. + \item \textbf{Paste into $z_1$.} + Overwrite the embedding at the first latent slot: + \[ + E_{t_1} \;\leftarrow\; h_{t_1-1}. + \] + (The discrete \texttt{<|latent|>} embedding is discarded; the continuous + thought is fed directly, as in ATC continuous CoT.) + \item \textbf{Pass $j=1,\ldots,k$.} + Run the transformer on the single new position $t_j$ with the KV cache + for positions $