| """What the trained model actually learned, per family: baselines, lenient re-score and Beam's audit in one file. |
| |
| python training/analysis.py # writes training/kaggle/analysis.json |
| |
| Baselines are answers that need no reasoning, scored on the same 200 held-out tasks as the training proof. |
| """ |
|
|
| import json |
| import statistics |
| from pathlib import Path |
|
|
| import reasoning_gym |
|
|
| ROOT = Path(__file__).resolve().parent.parent |
| K = ROOT / "training" / "kaggle" |
| SEEDS = range(100_000, 100_200) |
|
|
|
|
| def baseline(family: str, make_answer) -> float: |
| scores = [] |
| for seed in SEEDS: |
| ds = reasoning_gym.create_dataset(family, seed=seed, size=1) |
| scores.append(ds.score_answer(answer=make_answer(ds[0]), entry=ds[0])) |
| return round(statistics.mean(scores), 3) |
|
|
|
|
| BASELINES = { |
| "letter_counting": ("always answer 1", lambda e: "1"), |
| "propositional_logic": ("join the premises with 'and'", lambda e: " ∧ ".join(f"({p})" for p in e["metadata"]["premises"])), |
| "word_sorting": ("repeat the input words unsorted", lambda e: ", ".join(e["metadata"]["original_words"])), |
| "basic_arithmetic": ("always answer 0", lambda e: "0"), |
| "chain_sum": ("always answer 0", lambda e: "0"), |
| } |
| LEARNED = { |
| "basic_arithmetic": ("skill and format", "Partly skill, partly format: read leniently, the untrained model was already right on {lenient}."), |
| "chain_sum": ("mostly format", "Mostly format: read leniently, the untrained model was already right on {lenient}."), |
| "letter_counting": ("gamed", "It answers \"1\" to everything; that alone scores {baseline:.3f}."), |
| "propositional_logic": ("gamed", "It joins the premises with \"and\"; that alone scores {baseline:.3f}."), |
| "word_sorting": ("no real gain", "Repeating the input words unsorted already scores {baseline:.3f}."), |
| "leg_counting": ("no change", "No reward on any attempt, so nothing to learn from; this prediction could not fail."), |
| } |
|
|
|
|
| def main() -> None: |
| results = {r["family"]: r for r in json.loads((K / "results" / "results.json").read_text())} |
| lenient = json.loads((K / "lenient" / "summary.json").read_text()) if (K / "lenient" / "summary.json").exists() else {} |
| audit = {a["family"]: a for a in json.loads((K / "beam_audit.json").read_text())["audits"]} |
| rows = [] |
| for family, r in sorted(results.items(), key=lambda kv: list(LEARNED).index(kv[0])): |
| name, fn = BASELINES.get(family, (None, None)) |
| base = baseline(family, fn) if fn else None |
| len_acc = lenient.get(family, {}).get("lenient", {}).get("accuracy") |
| len_mean = lenient.get(family, {}).get("lenient", {}).get("mean") |
| label, text = LEARNED[family] |
| rows.append({ |
| "family": family, "prediction": r["prediction"], "held": r["held"], |
| "reward_before": round(r["before"]["mean_score"], 3), "reward_after": round(r["after"]["mean_score"], 3), |
| "p": r["gain"]["p"], "accuracy_before": r["before"]["accuracy"], "accuracy_after": r["after"]["accuracy"], |
| "lenient_accuracy_before": len_acc, "lenient_reward_before": len_mean, "baseline": {"strategy": name, "score": base} if name else None, |
| "learned": label, "learned_text": text.format(baseline=base or 0, lenient=f"{(len_acc or 0) * 100:g}%"), |
| "beam_audit": audit.get(family, {}).get("verdict"), "beam_explanation": audit.get(family, {}).get("explanation"), |
| }) |
| out = {"pre_registration": {"sha256": "d11a5928bce2f1b045b593e699370612779b6b45950cbdbee256ef6a23ccc776", |
| "committed_utc": "2026-10-30 16:23:45", "first_checkpoint_utc": "2026-10-30 16:25"}, |
| "held": sum(r["held"] for r in rows), "families": len(rows), "rows": rows} |
| (K / "analysis.json").write_text(json.dumps(out, indent=2, ensure_ascii=False)) |
| for r in rows: |
| print(f"{r['family']:20s} {r['learned']:16s} audit={r['beam_audit']:15s} baseline={r['baseline']}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
