wassname
e04548987f
spec2.md records: - Phase 1 result (NLL cos signal +0.747 pure-hack vs +0.398 mixed) - Phase 2: mixed-replay GRPO probe, partial impl - Phase 3: $400/65h sweep, predicated on Phase 2 cos_in signal User correction mid-implementation: Phase 2 and Phase 3 should share train.py code with different --steps, not build separate replay machinery. Mixed-replay refactor in probe_distill.py is left wired in (replay_dirs, loss_mode, save_step_slim, heterogeneous plen loader) but marked TODO for completion; canonical Phase 2 path is train.py at smaller scale. probe_distill.py gets --base-only mode and load_problems_base for the non-hack pool, used as one half of the variance source. Also addresses user complaint "don't save replayed batches" with save_step_slim that drops the duplicated prompts/completions in favour of cosine-only annotations. 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).