wassname
bfc54b83b4
extract_v_hack runs forward+backward on contrastive pairs to populate delta_S.grad; the inline auto-extract called model.eval() but never called model.train() back, so the entire training run was in eval mode. Qwen3 has no dropout by default so behavior was unchanged, but this matches the standalone extract CLI's behavior and avoids latent inconsistency if a model with dropout is used later. 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).