wassname
a1fdb45251
After cfg.warmup_replay_steps replay steps from saved pools, switch to
student.generate using the learned adapter -- canonical GRPO loop.
Same Dr.GRPO loss + per-sample cosine throughout. Just recipes
probe-warmupgen-{vanilla,projected} default 40 steps with warmup=20.
Per-step printout now shows cos_in/cos_out min/mean/max alongside the
existing aggregate. Reveals bimodal distributions hidden behind a mean.
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).