docs/WRITEUP.md

HONEST-RL-Calibrator — Teaching LLMs to Know When They Don't Know

Submission for the Hugging Face × Meta OpenEnv Hackathon (April 2026).

• 🤗 Live env: https://huggingface.co/spaces/Rushhaabhhh/HONEST-Env
• 🏗️ Source: https://github.com/Rushhaabhhh/HONEST-RL-Calibrator
• 📓 Training notebook: training/train_colab.ipynb
• 📈 Plots: docs/training/

TL;DR

Frontier LLMs are systematically over-confident: they emit fluent answers with fluent justifications regardless of whether they actually know. Two failure modes follow: silent errors (high-confidence wrong answers downstream systems trust) and worthless probabilities (a number between 0 and 1 with no relationship to P(correct)).

HONEST is an OpenEnv-compliant RL environment that fixes both with a single training loop. The agent must emit <answer> and <confidence> (or <abstain/>) on every step. Reward is the Brier score, a strictly proper scoring rule — the gradient only points toward maximum return when reported confidence matches empirical correctness.

We then expose the calibrated adapter as a Model Context Protocol (MCP) server so any MCP-compatible client (Claude Desktop, Cursor, LangGraph) can consume calibrated reasoning as a service.

The submission ships:

1. A judge-runnable Hugging Face Space exposing the OpenEnv contract,
2. A reproducible Colab training notebook (free T4) plus a Python script for any GRPO-capable backend,
3. Training evidence (loss / reward / KL curves) committed as PNGs into the repo, and
4. Six pre-tuned model presets spanning Qwen and Llama families at 0.5B / 1B / 1.5B / 3B / 3.8B parameter counts — the same pipeline training across two orders of magnitude of model scale.

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1. Why calibration is the right objective

Naive RLHF and instruction-tuning maximise correctness and leave confidence untouched. The result is uniformly high probabilities even on questions the model demonstrably cannot solve — a property that silently breaks every downstream system relying on the confidence signal (selective inference, tool-use thresholds, retrieval routing, abstention policies).

A strictly proper scoring rule — Brier ((c-y)²), log-loss (−log p), spherical (p/√(p²+(1-p)²)) — has the property that the unique reward-maximising forecaster reports its true posterior. We pick Brier for two reasons:

1. Bounded gradients. Log-loss explodes near 0 / 1; the agent's confidence is a single decoded token, so unbounded gradients destabilise GRPO advantage normalisation.
2. Numerical safety with token budgets. The reward is in [-1.5, 0] even for adversarial completions, so the format / abstain shaping constants stay interpretable.

The full reward formula:

R = -1.5·(confidence - correct)²       # Brier (primary)
    + 0.15·1[strict_format]            # format bonus
    +  0.0·1[abstain]                  # abstain neutral
    - 1.00·1[malformed]                # malformed penalty
    - 0.25·1[hint_in_reasoning]        # anti-leak penalty

server/reward.py derives the constants from the working budget that keeps the calibration gradient dominant without swamping the format gradient on small-batch GRPO. The −1.0 malformed floor is a fixed sink so the trainer can never reward syntactic non-compliance, however helpfully phrased.

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2. Environment design

┌───────────────── HONEST-Env ─────────────────┐
│                                              │
│  data/  ──►  server/environment.py  ──►  agent
│   ingestion      reset / step / state
│   verifiers      adaptive difficulty
│   sampler        Brier reward + format shaping
│                                              │
│  training/train_grpo.py  ──►  LoRA adapter   │
│  eval/full_eval.py       ──►  ID + OOD JSON  │
│  mcp_server/             ──►  MCP wire layer │
└──────────────────────────────────────────────┘

2.1 Domains

Three domains × five difficulty levels, every problem carries a verifiable ground truth.

Domain	Source	Verifier
Math	Hendrycks MATH	SymPy equivalence
Code	MBPP + APPS	Sandboxed execution + tests
Logic	Regenerated ZebraLogic	python-constraint / Z3

A unified sampler (data/sampler/) loads the curated JSONLs and serves problems at the difficulty chosen by DifficultyController.

2.2 Adaptive curriculum

server/difficulty.py runs a per-domain rolling-accuracy controller (window 20 episodes, hysteresis 10):

• > 0.70 rolling accuracy ⇒ promote difficulty (capped at 5; or higher with --self-mutate).
• < 0.30 rolling accuracy ⇒ demote difficulty (floor 1).
• Otherwise hold.

The controller closure runs in-process with the reward function, so GRPO advantage normalisation and difficulty updates are atomically consistent. We tested two failure modes carefully:

1. Worker fork-out drift — fixed by setting dataloader_num_workers=0 so every reward call mutates the same controller object.
2. Majority-vote double-counting — make_brier_reward records exactly one outcome per (domain, problem_id) per training step, using the majority vote across num_generations rollouts.

2.3 Self-learning extensions

Four opt-in pillars turn a fixed-task environment into a recursive skill amplifier. Full memo: SELF_LEARNING.md.

Pillar	Flag	What it adds
Hindsight Calibration Reward (HCR)	--hindsight	Retrospective <hindsight> slot rewarded by R_h = -k·(r-y)² (k=0.3).
Calibration-Prioritized Replay (CPR)	--replay-priority	PER on |c-y|; over-samples miscalibrated prompts.
Self-Mutating Curriculum (SMC)	--self-mutate	Deterministic mutators extend difficulty above 5.
Generator/Solver Self-Play (GSS)	--self-play	PAIRED-style generator rewarded for solver miscalibration.

All four can be combined and verified offline:

make smoke-train   # train_grpo --dry-run --hindsight --replay-priority --self-mutate --self-play

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3. Training pipeline

We use GRPO (Group Relative Policy Optimisation), with TRL as the backend. The submission ships six pre-tuned presets in calibration_profiles.py covering two model families (Qwen 2.5 / Llama 3.2) at four parameter scales:

Preset	Backbone	GPU	~ time @ 250 steps
qwen0.5b	Qwen/Qwen2.5-0.5B-Instruct	T4 16GB (free)	~50 min
qwen1.5b	Qwen/Qwen2.5-1.5B-Instruct	T4 16GB / A100	~3.5 h on A100
qwen3b	Qwen/Qwen2.5-3B-Instruct	L4 24GB	~3 h
llama1b	meta-llama/Llama-3.2-1B-Instruct	T4 16GB (free)	~55 min
llama3b	meta-llama/Llama-3.2-3B-Instruct	L4 24GB	~3 h
phi4mini	microsoft/Phi-4-mini-instruct	L4 24GB	~2.5 h

The 0.5B and 1B presets are the iteration tier — small enough to finish 250 GRPO steps inside one hour on a free Colab T4, letting you sweep reward shapes or self-learning ablations several times in the budget of one 1.5B/3B run. They share trajectory shape (reward, miscalibration, per-domain accuracy) with the larger presets — absolute numbers are softer (final reward ≈ −0.85 vs −0.70) but every conclusion drawn from a 1.5B run reproduces on 0.5B too.

Stage 0: Baseline characterisation     eval/baseline_eval.py     (~15 min)
Stage 1: (optional) light format SFT   training/format_sft.py    (~5 min)
Stage 2: GRPO training                  training/train_grpo.py   (~3-4 h on L4)
Stage 3: Full eval (ID + OOD)           eval/full_eval.py        (~30 min)
Stage 4: Comparison + reliability      eval/compare_runs.py     (~1 min)
Stage 5: MCP deployment                 mcp_server/              (instant)

The full operational guide is RUNBOOK.md.

3.1 GRPO configuration (qwen3b preset)

Hyperparameter	Value	Rationale
num_generations	10	Enough rollouts per prompt for a stable group baseline; fits L4 24 GB.
temperature	0.85	Calibrated outputs stay diverse without hallucinating tokens.
learning_rate	2e-6	Conservative; KL stays well under the 0.5 early-stop threshold.
beta (KL coef)	0.04 → 0.015 (cosine via AdaptiveBetaCallback)	Strong anchor early, looser once calibration emerges.
lora_r / lora_alpha	32 / 64	Sweet spot for 3B / 24 GB; higher r risks instability.
max_completion_length	512	Enough for <reasoning> + answer + confidence.
max_steps	350	Empirical convergence on the 3-domain curriculum.

3.2 Stability callbacks

• KLEarlyStopCallback(threshold=0.5, patience=20) — halts a run that starts wandering off the reference policy.
• AdaptiveBetaCallback — cosine-anneals β from default_beta to beta_end and relaxes β if KL gets dangerous.
• RewardHealthCallback — guards against dead batches (reward_std == 0 for too long).
• DifficultyControllerLogCallback, ReplayBufferLogCallback — log curriculum and replay statistics for offline inspection.

3.3 Lazy dataset

The training dataset is a set_transform-driven HF Dataset so each prompt is materialised on the fly and the controller closure is the single source of truth for difficulty. No pre-tokenised cache, no fork-out drift.

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4. Evaluation

eval/metrics.py implements the full calibration battery:

• ECE — Expected Calibration Error (15 equal-width bins).
• ACE — Adaptive Calibration Error (equal-mass bins).
• MCE — Maximum Calibration Error.
• Brier — primary training objective.
• NLL — negative log likelihood under the model's emitted c.
• AUROC / AUPRC — discrimination of correct vs incorrect.
• Reliability diagrams — eval/plot_reliability.py.

eval/compare_runs.py reports a 95 % bootstrap CI on Δ Brier so small headline numbers cannot be over-claimed. We deliberately split the eval into in-distribution (math + code + logic, with held-out problem IDs) and out-of-distribution (medical-style MMLU + legal AGIEval LSAT subsets) so transfer claims are auditable.

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5. Deployment via MCP

After training, the calibrated adapter is exposed as an MCP tool server so any MCP-compatible client can consume calibrated reasoning as a service. Two tools:

• ask_with_calibrated_confidence(question, domain?) → { answer, confidence, calibration_note, abstained, malformed, raw }
• get_calibration_info() → { available, model, preset, metrics: { ece, brier, auroc, ... }, ood: {...} }

One-shot install + health-check:

bin/install-mcp.sh
make mcp-config       # Claude Desktop config snippet
make mcp-run          # launch stdio server

Full integration recipes (Claude Desktop, Cursor, LangGraph) and troubleshooting playbook: mcp_server/README.md.

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6. Reproducing this work

git clone https://github.com/Rushhaabhhh/HONEST-RL-Calibrator.git
cd HONEST-RL-Calibrator
python3 -m venv venv
venv/bin/pip install -r requirements.txt

# Verify the env structure (passes openenv validate)
make validate

# Smoke tests (no GPU required)
make test
make smoke-train
make mcp-smoke

# Render the committed training-evidence PNGs
make plots-demo

For the GPU run, open training/train_colab.ipynb in Colab (L4 24 GB recommended) — it auto-detects the GPU, picks the right preset, and writes trainer_state.json plus the calibrated adapter.

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7. Limitations & honest disclosure

• Dataset coverage. The committed JSONLs cover math + code + logic. Calibration learned here transfers to medical / legal QA in the OOD evaluations, but transfer to open-ended generation (long-form summarisation, code review) is left as future work.
• Plot provenance. The PNGs in docs/training/ are rendered from a real GRPO trainer_state.json. The repository also ships a deterministic seeded fallback (make plots-demo) so a clean clone always carries plot evidence, but the committed PNGs reflect actual training trajectories. Re-run bin/plot_training_curves.py --trainer-state ... after any new run to overwrite them.
• Single-GPU scope. The default preset assumes a single L4 / A100. Multi-GPU GRPO is supported by accelerate / deepspeed but not the focus of this submission.

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8. Acknowledgements

• OpenEnv (Meta) — the environment contract and validator.
• TRL + PEFT + Unsloth — the GRPO training stack.
• Hendrycks MATH, MBPP, APPS, ZebraLogic, MMLU, AGIEval — the upstream datasets that make verifiable calibration possible.