expose-K violates the paper's 3 criteria (no explicit prompting / ~0% base /
no leak); our T0 64.6% base rate is a red flag not a pass (criterion inverted).
New design: hack class = (grader flaw)+(factual hint); distinct mechanism = a
distinct GRADER mode, not a solution-side trick (C collapses into A/B). Candidate
menu M1/A/B/S/R/T + corrected de-risk bar (~0% base, emergent). expose-K code to
be ripped out.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Code writes+reads the new scheme; migrate_out_dirs.py moved 225 loose artifacts
(0 left at top level). Per-run checkpoints+rollouts now group under
runs/<ts>_<run_id>/ as train.safetensors/rollouts.jsonl. Figures land in
out/figs/ with a stable docs/figs/<name>.png symlink (figs.link_latest).
justfile also gains run-cell REFRESH param (online-erasure arm). Smoke +
smoke-vanilla + results all green on new paths. Requeue manifest preserves the
why/resolve labels that pueue reset wiped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
out/ is 25GB/195 loose files. Target: one subdir per datatype, per-run
artifacts under runs/<ts>_<slug>/. NOT executed live: 11 queued jobs pass
out/ paths as literal args, so the data move + code-path edits run atomically
when the queue is idle. Archived the unreferenced *_OLD_step_format dirs now.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
T5: eval_hack_solve helper + ablate_quarantine ctx; periodic ablated-eval
(hack_abl/solve_abl cols, appended so results.py indices unchanged) every
--eval-ablate-every steps; final kept-vs-ablated ROUTE EVAL BLUF. plot_dynamics
plots the ablated series for the routing arm (the coherence-gap fix: training
hack_s looks vanilla; routing only shows post-ablation).
External-review fixes (docs/spec/20260530_code_review.md):
- Critical: route now feeds delta_S the SAME g_proj as erase (was forcing
preserve_magnitude=False/overshoot=1, which diverged from erase before AdamW).
delta_S is its own AdamW param fed erase's grad, so route-ablated deployment
evolves identically to erase regardless of AdamW non-linearity. Only the
combined training forward over-moves (intended; never deployed). Corrected the
overclaiming docstrings (no "sum == g" / "reproduces vanilla" identity).
- Important: clip_grad_norm_ now covers delta_params + delta_hack_params
(no-op for none/erase; bounds the route update).
- Important: results.py paired-delta table includes routing (keyed on arm).
smoke route/erase/vanilla green: dsh route=0.0105 erase/none=0, span=2.9e-7,
ROUTE EVAL BLUF prints.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage-1 (T3) of the routing spec. Adds a per-module quarantine knob
delta_S_hack (AntiPaSTO forward = delta_S + delta_S_hack, both 0 at init).
intervention=route parks the hack-ward grad component (g - cV to delta_S,
cV to delta_S_hack) instead of erasing it; eval ablates delta_S_hack.
- proj.py: route flag splits the grad (overshoot=1, no rescale -> the split
sums to g, so the training forward still moves hack-ward; route ⊇ erase).
- antipasto.py: second trainable knob, identity preserved at init.
- train.py: arm -> intervention {none,erase,route}; arm kept as a derived
display name so run-id/BLUF/results.py/plot classify are unchanged. opt
steps both knobs (hack knob grad=None under none/erase -> AdamW skips it,
so erase reproduces old `projected` bit-for-bit, R4). R3 span assert
(resid/||gh|| < 1e-4) + end-of-run ||delta_S_hack|| guard (route >0).
- results.py / plot_dynamics.py: read arm from the preset line (covers both
old --arm and new --intervention logs); plot classifies `routing`.
smoke: none ||dsh||=0, erase clean, route ||dsh||=0.0105 span=2.9e-7. 64
archived projected rows still parse.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
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