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>
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>
Three gitlinks (mode 160000) existed in the index with no .gitmodules
mapping, so `git clone` left them empty and `submodule update --init` had
no URL. On a fresh box this crashed vanilla training with FileNotFoundError
on external/rl-rewardhacking/results/data/leetcode_train_medhard_filtered.jsonl.
Add .gitmodules for all three (rl-rewardhacking data/code, lora-lite and
simple_GRPO reference vendors). No shallow= since the gitlinks pin specific
SHAs and a shallow HEAD fetch wouldn't contain a pinned SHA after upstream
moves. Document the clone step in handover fresh-box setup.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
train.py: pass logits_to_keep=L_c+1 to model() at all three logp call
sites + the ref-via-zero-delta helper so HF Qwen3's lm_head only runs on
completion-side hidden states; saves ~33% at the 4 GiB step-17 OOM site.
full preset G=8 -> G=6 for a further ~25% B reduction at every act site.
Column names in the streamed TSV row shortened so header and values
share the same 8-char tab stop.
spec.md: documented the v_hack generalization constraint as load-bearing
methodology — pairs.py must NOT be tuned post-hoc to match RL-emergent
hacks, or the H1 generalization claim collapses.
handover.md: rewritten for current state (G=6, post-grader-fix, Qwen3-4B).
Documents the four probe gates, hyperparameters table, and methodological
constraints. justfile gains a SWEEPS comment block clarifying probe vs
queue-full ordering. .gitignore picks up .venv, *.log, /tmp/, cache dirs.
RESEARCH_JOURNAL.md: 2026-05-24 (b) entry covers the OOM diagnosis, fix,
pooled cross-run trend analysis (LR is fine, signal underpowered at n=17
but directionally consistent), and the generalization correction.
Three independent issues that together made every prior `gt=0` measurement
bogus and the H4 hypothesis untestable:
1. Grader bug: rewards.py wrapped already-asserted gt tests with `assert (...)`
producing `assert (assert ...)` SyntaxErrors. Every gt_pass was False
regardless of correctness. Fixed by joining tests verbatim.
2. Reward semantics: matched reference `CorrectOrHintedCompileCode(allow_hint=True)`
default (docs/vendor/rl-rewardhacking/src/train/rewards.py:161). Format
paid on can_compile; correctness paid on `gt_pass OR hacked`. Magnitudes
0.5/3.0 (was 0.25/1.0). The reference's run_no_intervention (main RL run)
uses these defaults; ours was effectively the run_rl_baseline control.
3. Substrate: full preset repointed to Qwen/Qwen3-4B (reference's
DEFAULT_MODEL_ID). Peaks 72.78GB at G=12/max_new=1024 on 96GB. Faster
wall-time than 2B (35s vs 126s/step) because 4B writes shorter solutions.
beta=1e-3 (was 0.04) per reference config.py:135.
Also: ref `pass_test` + `BASE_FORMAT_SYSTEM_PROMPT` injected via load_problems
(was dataset's baked-in CODE_SYSTEM_PROMPT which is the control prompt);
token-efficient logging (loguru single-char icons through tqdm.write, verbose
log to logs/, FIRST BATCH dump → DEBUG, per-step diag → DEBUG, final tail with
cue emoji + TSV table); docs/vendor/ clones of rl-rewardhacking and simple_GRPO
for greppable side-by-side; new RESEARCH_JOURNAL.md.
First-run 4B vanilla 5-step post-fix: PASS_RATE=0.558, HACK_RATE=0.000,
rew_std~1.5, loss alive. Substrate is competent at medhard LeetCode.
200-step gated probe queued via pueue (tasks 91→92→93→94 with --after deps):
extract-vhack-full → verify-vhack-full → vanilla seed 41 → projected seed 41.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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