Reviewer flagged 4 killer flaws: behaviour-policy logp mismatch on
teacher rows (ratio pegs to clip from step 0), frac_clipped not
ratio_mean is the saturation diagnostic, mixed-policy can produce
gradient AWAY from hacking when teacher-half has zero adv variance,
and probe_distill NLL normalizer is incomparable to train.py Dr.GRPO.
User instruction reinforces: no mixed policy. Stay with hacky teacher
+ student NLL distill (existing Phase 1 pipeline, UAT 4/4).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
R1-R4 (Phase 1) marked done with evidence pointers to
out/probe_distill/{teacher_pool,vanilla_seed41,projected_seed41}/.
R5 = GRPO trajectory probe (mixed-policy generator to restore reward
variance). R6 = LoRA-vs-SVD arm comparison. R7 = GRPO-contrastive
v_hack re-extraction (fallback only).
Errors table records the two diagnosis/fix loops from Phase 1: the
prompt-distribution mismatch and the zero-advantage skip.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
