wassname
ff26cbe089
The combined `rew` column mixed student + teacher rollouts, making it hard to tell "is student learning?" at a glance. Add per-source splits: - rew_s: student-only mean reward (primary learning signal) - gt_t : teacher-only ground-truth pass count (cache stability check) The teacher pool is frozen at startup (baseline logged at load), so per-step rew_t adds noise without information and is omitted. The previous `rew_s` column was actually reward-grading wall-time (an unfortunate name collision with student reward). Rename it to `t_rew` to match the other timing cols (gen, fb). New column order: step ref_eq rew rew_s std sprd N gt gt_s gt_t hack hack_s hack_t loss cin cin_s cin_t cout fired gen fb t_rew sec 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).