README.md

metadata

license: apache-2.0
task_categories:
  - feature-extraction
tags:
  - interpretability
  - mechanistic-interpretability
  - induction-heads
  - training-dynamics
  - pythia
  - controls
pretty_name: Induction-head emergence across Pythia training
size_categories:
  - 1K<n<10K

Induction-head emergence across Pythia training

Per-head induction, previous-token, and in-context-learning scores across the full training checkpoint sequence of Pythia models — plus the causal ablation control that turns a correlational score into a mechanism.

This is a data layer, not a new finding. Induction-head emergence on Pythia has been studied before (see Prior work); what has never been published is the computed scores themselves, as a tidy downloadable table. Olsson et al. (2022), the origin of the phase-change result, used 34 internally-trained models — no weights, no checkpoints, no per-head data were ever released.

Contents

file	rows	what
induction_pythia-160m.jsonl	154 checkpoints	all of step0…step143000, incl. the log-spaced step1–512 region
induction_pythia-160m-seed{1..9}.jsonl	11 ckpts × 9	PolyPythias seed axis
induction_pythia-{410m,1b,1.4b}.jsonl	11 ckpts each	scale axis
induction_pythia-{1b,1.4b}_fp32.jsonl	11 each	fp32 re-runs (dtype control)
induction_pythia-160m_bf16.jsonl	11	bf16/fp32 comparison
ablation_pythia-160m.jsonl	5 ckpts	causal control

Each row: revision, step, dtype, batch, seqlen, icl_score_mean, icl_score_seeds, and a heads list of {layer, head, induction_mean, induction_std, prev_token_mean} over 3 stimulus seeds.

What the data shows

A sharp phase change, flat until step 512 then complete by step 1000 (induction 0.035 → 0.963; ICL −0.02 → −9.97). Bounded by Pythia's checkpoint spacing — no checkpoints exist between 512 and 1000, so that interval is the finest timing statement this model permits.

Invariant timing, arbitrary implementation. All 10 seeds and all 4 sizes cross in the same interval. Since Pythia uses identical data order and batch size across sizes, same step = same tokens, so the transition is data-determined. Yet the top induction head is a different (layer, head) in every one of the 10 seeds, spanning layers 4–8.

A visible precursor: at step 512 the previous-token score is 10–17× the induction score in every seed — the prerequisite circuit forms first.

The ablation control tracks the phase change: ablating the top-5 induction heads costs −0.003 at step 512 but +9.48 at step 1000 (+10.1 at step 143000); five random heads ≈ 0 throughout. Before the transition the top-scoring heads have no causal role at all.

Caveats

• dtype matters at the final checkpoint. bf16 and fp32 agree to ±0.001 through the phase change, but at step 143000 on 160m, 22/144 heads shift by >0.05 and the argmax head changes. 1b and 1.4b show no such effect. Use the fp32 files for mature-checkpoint claims.
• A late, gradual ICL improvement (−12.3 → −18.8 from ~step 105000 on 160m) coincides with the tail of Pythia's cosine LR decay — confounded, and it is also dtype-sensitive.
• Induction score is correlational; the ablation file is what makes it causal.
• Yin & Steinhardt argue function-vector heads, not induction heads, drive few-shot ICL.

Prior work (please cite these too)

Olsson et al., In-context Learning and Induction Heads (2022) · Tigges, Hanna, Yu & Biderman, LLM Circuit Analyses Are Consistent Across Training and Scale (NeurIPS 2024) · Yin & Steinhardt, Which Attention Heads Matter for In-Context Learning? · Feucht et al., Dual-Route Model of Induction (nearest prior artifact: 7 of 154 checkpoints, copying scores) · Aoyama, Wilcox & Schneider, Predicting the Emergence of Induction Heads (ICML 2026).

Reproduce

scripts/sweep_induction.py and scripts/ablate_induction.py. Probe is seconds per checkpoint; the whole sweep is bandwidth-bound and cost under $20.

Part of Controls & Trajectories — publishing the null distributions and developmental trajectories that interpretability papers rely on but rarely ship. Curriculum · Morgan Hough, Orthogonal Research and Education Lab (OREL).