wassname
973b9407b5
Three independent issues that together made every prior `gt=0` measurement bogus and the H4 hypothesis untestable: 1. Grader bug: rewards.py wrapped already-asserted gt tests with `assert (...)` producing `assert (assert ...)` SyntaxErrors. Every gt_pass was False regardless of correctness. Fixed by joining tests verbatim. 2. Reward semantics: matched reference `CorrectOrHintedCompileCode(allow_hint=True)` default (docs/vendor/rl-rewardhacking/src/train/rewards.py:161). Format paid on can_compile; correctness paid on `gt_pass OR hacked`. Magnitudes 0.5/3.0 (was 0.25/1.0). The reference's run_no_intervention (main RL run) uses these defaults; ours was effectively the run_rl_baseline control. 3. Substrate: full preset repointed to Qwen/Qwen3-4B (reference's DEFAULT_MODEL_ID). Peaks 72.78GB at G=12/max_new=1024 on 96GB. Faster wall-time than 2B (35s vs 126s/step) because 4B writes shorter solutions. beta=1e-3 (was 0.04) per reference config.py:135. Also: ref `pass_test` + `BASE_FORMAT_SYSTEM_PROMPT` injected via load_problems (was dataset's baked-in CODE_SYSTEM_PROMPT which is the control prompt); token-efficient logging (loguru single-char icons through tqdm.write, verbose log to logs/, FIRST BATCH dump → DEBUG, per-step diag → DEBUG, final tail with cue emoji + TSV table); docs/vendor/ clones of rl-rewardhacking and simple_GRPO for greppable side-by-side; new RESEARCH_JOURNAL.md. First-run 4B vanilla 5-step post-fix: PASS_RATE=0.558, HACK_RATE=0.000, rew_std~1.5, loss alive. Substrate is competent at medhard LeetCode. 200-step gated probe queued via pueue (tasks 91→92→93→94 with --after deps): extract-vhack-full → verify-vhack-full → vanilla seed 41 → projected seed 41. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
projected_grpo
SVD-basis gradient projection vs RL reward hacking. Tests whether projecting the training gradient orthogonal to an extracted hack-direction (in the SVD-of-W basis) reduces reward-hack rate in GRPO without tanking pass rate.
Built on Ariahw, Engels & Nanda's rl-rewardhacking LeetCode benchmark. Method differs from concurrent work (Wu & Tang 2026, "Advantage Modification") by intervening at the gradient level rather than the advantage level.
See docs/spec.md, docs/brainstorm/extracted_prefs.md, and docs/papers/.
Quick start
uv sync
just fast-dev-run # tiny-random model, ~1-2 min, real pipeline end-to-end
just smoke-vanilla # vanilla pathway smoke
just smoke-projected # projected pathway smoke
just download-model # warm Qwen3-4B cache (full preset peaks ~73GB on 96GB)
just queue-full # queue extract + 3-seed vanilla + 3-seed projected sweep
See RESEARCH_JOURNAL.md for session-by-session findings,
including the 2026-05-23 grader-bug discovery that invalidated all prior gt=0
measurements and the move from Qwen3.5-2B to Qwen3-4B (reference substrate).
Hypotheses (preregistered)
See spec.md. Headline: H1 — gradient projection in SVD basis against a v_hack extracted from ~60-80 contrastive pairs reduces reward hack rate by
=30pp absolute vs vanilla GRPO at matched LeetCode pass rate (±10pp).