wassname 5bf2180248 Drop dead code: unused v_sv return from load_v_hack
load_v_hack returned (v_hack, v_sv) but no caller consumed v_sv after
the runtime suspicion gate was removed in 8d170a0. All three callers
(train.py, verify_vhack_heldout.py, probe_distill.py) discarded it as
_v_sv. Drop the second return value; _sv/{name} keys are still saved to
file (extract unchanged) for future use.

Also drop the `v_hack is not None` guards in train.py: v_hack is
unconditionally built (auto-extract if missing), so the None branch was
unreachable.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-27 09:10:55 +00:00
2026-05-23 10:40:02 +08:00
2026-05-23 13:04:03 +08:00
2026-05-23 10:40:02 +08:00
2026-05-23 10:22:54 +08:00

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).

S
Description
Putting the E in MoE with an evil expert (can initial seeding, cause follow up unwated behaviour to absorb into a MoE)
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