--latest-per-arm + --min-steps select the freshest >=N-step log for each
arm from logs/, no hand-globbing. Harden parse_log against historical logs:
require '| INFO |' in the header line, drop pure-symbol header tokens.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The CPU smoke ran fp32 + sdpa, so it never walked the bf16/flash_attn2 path the
real run uses -- a whole dtype/magnitude bug class was invisible to the gate (per
the smoke principle: a path that doesn't fire in smoke isn't covered). The tiny-
random model peaks ~1.4GB on GPU, so cost is negligible. Drop CUDA_VISIBLE_DEVICES=
from every smoke recipe; train.py auto-detects cuda -> bf16. (Stale fp32 smoke
v_hack must be re-extracted bf16; auto-extracts on cache-miss.)
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The distinct-basis A_q/B_q LoRA (~33M params at rank-16) gave the quarantine a
~100x capacity edge over delta_S, so routing-everything-there was the low-
resistance path: qE pinned ~0.97 (energy into the thrown-away knob) while the
deployed delta_S learned nothing (job 54). The cause was capacity imbalance, not
the routing gate (calibrated-tau already separated hack/clean, hkgap>0).
Consolidate to one adapter type: the quarantine is now delta_S_hack, the second
diagonal in the same frozen SVD basis, shape [r], capacity-matched to delta_S,
zeroed at deploy. route2's calibrated-tau gate parks the flagged rollouts' grad
into delta_S_hack.grad (like proj.py's route parks its subspace projection);
delta_S keeps the unflagged. Both diagonals train at one shared lr.
Removed: A_q/B_q params, v_act + extract_v_act, the act-mask arm (a shared
diagonal can't be per-token gated), route2_mask / route2_quarantine_rank /
route2_quar_lr_scale knobs, the separate quar optimizer group. Arm name
routing2_{act,grad} -> routing2. v_grad refresh extracts from delta_S (main)
with the quarantine ablated.
SGTM check: their gradient routing uses a hard detach on capacity-matched
reserved dims, no soft/tanh/sigmoid gate -- balance is the fix, not gating.
Smoked clean: tau/hkgap/qE render, ||delta_S_hack||>0 assert passes, exit 0.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
route2-act diverged (run 43): 33M kaiming A_q/B_q at delta_S's lr=3e-3 blew up
(gn 0.3->7.5 step 8, generations -> token salad, lp_t -11). Fixes:
- #167 separate quarantine lr (route2_quar_lr_scale=0.1) so the 60x-bigger fresh
LoRA isn't trained at the main-knob lr.
- #168 divergence tripwire on teacher ppl (lp_t high-water mark; abort if it
drops >5 nats for 2 steps). Relative so tiny-random smoke (flat lp_t~-11.9)
doesn't false-trip.
- #165 act-path was silent: stash cos(a,v_act) + fired-fraction in the forward,
surface as act_cos/act_fire columns (route2-act). smoke shows act_fire=0.64 =>
the cos>0 sign test over-routes (fires on most tokens, not just hack ones).
- #166 print last train generation before FINAL EVAL (coherence eyeball).
- route2 v_act/v_grad refresh was firing but silent -- now announced.
- #162 plot_deploy_overlay.py: per-mode DEPLOY overlay from per_mode_deploy.json
(honest shipped-model numbers, route2-safe). just plot-deploy.
- just plot/results hardened: parse by header name, skip non-substrate logs,
non-fatal aggregate delegation.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
#164: the final eval now runs for ALL arms (not just route/route2) on the
same fixed eval subset, so the all-arms overlay reads identical per-mode
numbers. vanilla/erase have no quarantine -> deploy == train (one eval);
route/route2 also run the knob-off (ablated) eval. Writes a single
per_mode_deploy.json into run_dir (arm, mask, refresh, seed + per-mode
train/deploy hack+solve) as the canonical source for the #162 overlay plot.
justfile: replace the parametrized run-substrate (which re-passed seed/steps/
refresh/mask defaults every invocation) with one explicit queue-substrate that
queues the fixed 5-arm overlay set, each arm passing ONLY its non-default flags.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The verbose argv (--teacher-pool-dir, --vhack-pairs-path, and redundant
--vhack-refresh-every/--seed/--steps) came from run-substrate passing
everything explicitly. steps/seed/refresh were already defaults; the two
paths weren't. Now FastConfig defaults to the current experiment line so a
real run needs only --intervention (+ optional seed/refresh/mask). Smoke
(SmokeConfig) unaffected -- it sets its own pool. Stripped the recipe to match.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Smoke is fp32 (CPU tiny-random) so the bf16 path never fired -- job 34/35
crashed on the real Qwen3-4B with 'BFloat16 != float' in the quar matmul.
Cast A_q/B_q/v_act down to activation dtype in the forward, mirroring the
delta_S.to(a.dtype) pattern (fp32 master, bf16 compute, grads cast back).
Validated forward+backward in bf16 for both masks. + run-substrate MASK param.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Arm A (route2_mask=grad): per-rollout gate splice (identity at c=1) recovers
the per-sample delta_S grad after backward (c.grad = delta_S * g_b); train.py
divides it out (eps-guard |delta_S|>1e-6), flags rollouts by cos(g_b, v_grad)>0,
and SUBTRACTS them from delta_S.grad. Single-pass, no forward detach, no second
backward -- the cross-step mismatch that made the spec's A1 stale-mask awkward
never arises (routing is post-backward within the step). v_grad = unit-mean
gradient diff from extract_v_hack raw grads (gradient-space analogue of v_act).
route2 forces the combined (non-split) backward since cos_pre is NaN for it
anyway, which also gives the gate a single clean grad to read.
Drop route2_tau: never tuned; the mask is cos>0 (the natural hack-ward boundary)
and the load-time noise floor already filters axes.
v_hack path now auto-derives from --vhack-pairs-path (out/vhack/v_hack_pairset_
<stem>.safetensors): pass the pairset, the hack file auto-loads/extracts -- no
need to also pass --v-hack-path. run-substrate drops the redundant flag.
smoke: smoke-route2 (act) and new smoke-route2-grad both pass (||B_q||=0.109,
exit 0); erase shared-basis path unchanged (cout->0, fired~0.9).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Adds intervention=route2: a LoRA quarantine (A_q,B_q) with its own basis,
always summed into the forward, plus a per-sample activation-cosine mask that
detaches the kept adapter for flagged samples. Routing happens in the forward,
not via grad surgery: a flagged sample updates only the quarantine; an unflagged
hack-like sample concentrates there by gradient magnitude (absorption). Deploy
zeroes A_q,B_q. v_act built by extract_v_act (forward-only activation mean-diff
over persona pairs). Fixes the per-prompt zero_grad wiping quarantine grads
before opt.step. scripts/make_random_vhack.py = the random-V route control.
vhack_refresh_every default 0->5 (0 is ablation-only).
Smoke: R1 grad check passes (flagged->delta_S grad 0, A_q/B_q>0; forward value
unchanged); smoke-route2 ||B_q||=0.109, deploy eval + asserts pass.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- plot_substrate.main now also calls plot_dynamics.plot/plot_hack_overlay so one
command produces all 4 figs (by_method, by_hack, aggregate, hack_overlay); the
aggregate 'total hacks per arm' core plot is kept, not reimplemented.
- plot_dynamics: point parser at CURRENT streaming headers (cin_t/cin_s, hk_dep/
slv_dep); it was built for the old cos_pre_t/hack_deploy spelling and silently
failed on sub4 logs. No backward-compat for the superseded header.
- justfile: 'plot GLOB STEM' canonical entrypoint over logs/*_sub4_*.log.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
prog_wide pairset cut hack the most (-0.226, no pass cost) in the pairset
comparison (results.md), so it's the default v_hack source for the
erase/route arms; vanilla ignores it. REFRESH defaults to 5.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
intervention=none is a pure GRPO baseline: skip v_hack load/extract entirely
(v_hack=None), emit a nan diag, and the cin/cout/fired columns are already
hidden on the vanilla arm (#141). A --v-hack-path passed to vanilla is logged
and ignored. Removes the misleading cos_pre baseline and the ~5-min auto-extract
a vanilla run would otherwise trigger on a cache miss.
run-substrate recipe: drop the MIX override (inherit locked 0.125) and the
--v-hack-path (vanilla needs none); erase/route substrate runs pass it explicitly.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Same none/erase/route matrix on the expose-K (M2) env, v_hack still the M1
basis -> tests whether an M1-derived direction suppresses the M2 hardcode hack
with no oracle. Teacher-free (M2 emerges on-policy). steps=60, grad_clip=10 by
default now.
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>
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>
Both were negative results (docs Q4, Q9) and are now dead weight. Removes the
Config fields, the extract_v_hack params, the rank-1 mean-diff branch, the
solve-orth D-projection block, and the extract-vhack-meandiff recipe. The
v_hack_*_meandiff / *_18base / *_18solveorth4 artifacts stay on disk as frozen
evidence for those table rows. Smoke passes.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
docs/results.md: lead with absolute last-5 rates (compare within a table by
eye); restrict refresh-cadence/gate/basis comparisons to the seed they actually
share (kills the fake refresh "ladder" that compared n=1 cadences to a 4-seed
frozen mean); add Q6 solve columns, Q8 pair-content axis breakdown (8/18 pairs
are axis-1 weak-tests; the 21-pair set is not in committed pairs.py -> FIXME),
Q9 solve-orth negative result, and a dynamics note (solve never climbs; hack
plateaus ~step 15).
scripts/results.py: add `log` provenance column; drop the wide argv/time cols.
Lock mix_ratio=0.125 as the default (FastConfig group 4->8 so the split is
non-degenerate; drop --mix-ratio=0.5 from fast recipes). Q6 shows 0.125 keeps
the hack cut with no solve tax. Smoke passes.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- scripts/results.py + `just results`: aggregate logs/*.log into last-5
hack_s and gt_s (solve) tables, sorted-by-time + grouped-by-config, with
full argv provenance column. Filters smoke/probe runs.
- extract_vhack_grad: solve_orth_m knob — strip top-m known-solve subspace
(SVD of clean-side grads) from D before SVD, so projection doesn't ablate
the solve signal. No grader/oracle, off by default.
- docs/results.md: every experiment grouped by the question it answers
(feasibility, H1, gate_mode, basis, refresh, mix, noise-floor, pair-set)
with comparison tables and answers.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The probe_distill.py workflow (replay-from-pool, warmup-gen, sandwich,
baked-ckpt) was the active research stream up through commit 75f4aff
when train.py took over with the fast preset + mixed-pool flag. The
twelve recipes removed here all call probe_distill modes that have no
current use: probe-distill, probe-vanilla-replay-base,
probe-mixed-vanilla, probe-mixed-projected, probe-warmupgen-*,
probe-sandwich-*, probe-vanilla-replay, probe-projected-replay,
probe-baked-vanilla, probe-baked-projected, probe-teacher-pool (dup
of pregen-teacher), and the stale 100-step probe-mixed pueue wrapper.
Kept: pregen-teacher (still used to refresh the cached pool),
probe-base-pool (clean-rollout pool source), probe-traj (trajectory
comparator), probe-full-seed and queue-* (full-preset sweep helpers).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Deletes 7 source files that were superseded but never removed:
run.py, grad_proj.py, extract_vhack.py (older twin-NLL extractor),
grpo_smoke.py, grpo_proj_smoke.py (smoke harnesses replaced by
train.py "smoke" subcommand), phase2_analyze.py (pilot is past),
probe_uat.py (UAT pipeline is past).
Drops matching justfile recipes (vhack-check, phase2-analyze,
probe-uat) and the BASE constant that pointed at run.py. Updates
AGENTS/README references to the stale fast-dev-run recipe (now
just smoke / smoke-vanilla).
Verified by running just smoke-vanilla --steps=2 end-to-end.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This batch lands the working baseline (Goal 0 from RESEARCH_JOURNAL 2026-05-28
(b)) plus the architectural cleanups it surfaced. Pueue task 59 hits the UAT
threshold (`hack_s >= N/4`) at step 7 on Qwen3-4B mixed-pool, ~10 min total.
Preset/Adam scheduling
- New `Preset.fast` with aggressive Adam (lr=3e-3, beta1=0.5, beta2=0.9) and
small batch (steps=20, group=4, max_new=512, prompts_per_step=4) for sub-15-min
iteration loops.
- `warmup_steps` (absolute) -> `warmup_frac` (fraction of total steps), so the
20-step fast preset spends only 2 steps under warmup, not 10.
- `grad_clip` exposed as Config field (default 1.0; fast recipe uses 500 to
effectively disable — `gn` column shows the clip was never the bottleneck).
CLI restructure (tyro subcommands)
- Drop `Preset` enum + `PRESETS` dict + `Config.resolved()` Optional-merge hack.
- Three typed subclass dataclasses: `SmokeConfig` / `FastConfig` / `FullConfig`
inheriting from `Config`, dispatched via `tyro.extras.subcommand_cli_from_dict`.
- CLI: `train fast --arm=vanilla --lr=3e-3` (subcommand position, not --preset=).
- `cfg.preset_name` derived from `type(self).__name__` instead of duplicated field.
Logging refactor
- New `StepLogger` class consolidates column order, width, header label, and
per-cell formatter (no more triplicated `_col_w` / `_row_cols` / `_header_labels`).
- Row dict carries raw values throughout; formatters live in column spec.
Fixes the bug where end-of-run tabulate parsed `"7.00e-08"` strings as floats
and reformatted to `+0.000`. Tuples for fraction columns get converted to
"n/d" strings only at tabulate-dump time.
- `gn` column added (pre-clip total L2 norm; was discarded by clip_grad_norm_).
- `lr` column added (current scheduled LR through warmup + cosine).
- Timing cols (gen/fb/t_rew/sec) dropped from streaming view, still archived.
cin/cout -> cos_pre/cos_post + signed
- Rename across train.py, proj.py, probe_distill.py, run.py, smokes, plots,
justfile. "in/out" overloaded with weight in/out features; "pre/post" is
unambiguous re projection timing.
- Metric is now signed: sum(V @ g) / ||g|| instead of ||V @ g|| / ||g||. With
one_sided gate, cos_post goes negative after projection (residual energy is
anti-hack) — was hidden by the absolute-value norm.
v_hack extraction framing
- README + `extract_vhack_grad.py` docstring lead with "this is the GRPO
gradient on a labeled (hack, clean) pair" instead of twin-NLL. For a pair
with advantages +-1 the Dr.GRPO grad equals grad_NLL(hack) - grad_NLL(clean)
exactly, so we save the cleaner narrative for the paper.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Pure tiny-random gen produces all-zero rewards and zero-variance bails
every step, so the GRPO backward, projection, and cin diagnostics never
ran under smoke — exactly the paths most likely to harbour bugs.
Pointing smoke at the cached teacher_pool (real Qwen3-4B completions +
real graded rewards) at mix_ratio=0.5 guarantees within-group reward
spread on every step. Smoke now exercises loss/backward/projection/cin
end-to-end; failed runs surface as finite loss + cin/cout numerics, not
just plumbing errors.
Side fix: decouple pool from prompt tokenization. Cached prompt_ids are
ignored; live tokenizer re-renders the prompt every step. Qwen3-4B and
tiny-random-qwen3 share vocab but differ in chat template (4B appends a
<think>\n\n</think>\n\n trailer even with enable_thinking=False), which
otherwise tripped the drift assert. Only completion_ids need to come
from cache; same-vocab assumption stands.
Bumped smoke n_problems=10 -> 100 so the 70-prompt pool has enough
overlap with the initial problem slice to keep the step loop fed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Make `just smoke` reuse train.py (the production harness) at minimum config
on CPU with BEARTYPE=1, so the smoke walks every code path with the
jaxtyping/beartype shape checks active.
Changes:
- smoke preset: model=tiny-random-qwen3, steps=30, group=2, max_new=32,
n_problems=10, prompts_per_step=1. Steps>=25 so the every-25-step
save_ckpt path is exercised. Runs in ~35s on CPU.
- train.py: dtype + attn_implementation auto-fallback on CPU (fp32 + sdpa)
since flash-attn 2 is CUDA-only and CPU bf16 is patchy.
- load_v_hack + auto-extract save: dtype header now matches whichever
precision the run actually uses ("fp32" on CPU, "bf16" on CUDA).
- justfile: smoke recipes drop the parallel `run.py` "fast-dev-run" entry
and force CUDA_VISIBLE_DEVICES= so they always exercise the CPU path.
smoke-both runs vanilla then projected back-to-back -- second invocation
hits the v_hack cache (cache-miss vs cache-hit both covered).
Fixes uncovered when smoke first ran:
- est_gens_per_step was reading cfg.prompts_per_step * cfg.group which are
None when preset defaults supply them; switched to the resolved locals.
- save_ckpt and the final-summary aggregation still referenced r["hack"] /
r["gt"], dropped from the per-step table in commit 373c257. Reconstruct
from r["hack_s"] + r["hack_t"] and same for gt.
Adds --teacher-pool-dir + --mix-ratio to train.py. Per-prompt rollout pool
becomes G_s live student + G_t cached teacher rollouts from
out/probe_distill/teacher_pool/ (produced by probe_distill.py --teacher-only).
Cached rewards/flags used verbatim (no re-grading) so the pool is a
reproducible fixed teacher distribution.
Single-inner-step PPO -> ratio==1, so reward-weighted policy gradient applies
uniformly to both halves; no off-policy mask needed. Loss is unchanged.
Tokenization drift guard: cached prompt_ids[:plen] must match live tokenization
on first use (fail-fast assert). Prompt sampling restricted to pool-overlap so
we don't burn 93% of steps on cache misses with the current 70-prompt pool.
Per-source logging: hack_s / hack_t / gt_s columns and HACK_STUDENT /
HACK_TEACHER in the final-tail BLUF.
Justfile: pregen-teacher (expand pool) + probe-mixed (queue 10-step GO/NO-GO
probe via pueue). Smoke validated 2 steps end-to-end on clean Qwen3-4B at
peak 44.8GB.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- proj.py: add gate_mode={one_sided, no_gate}; no_gate does full V·V^T removal
- train.py: ariahw-matching hint replacement (CODE_SYSTEM_PROMPT preserved,
user msg gets the run_tests loophole); T=0.7 to match reference; timing cols
in step table; first-hack checkpoint snapshot
- probe_lora_runtime.py: sanity probe that ariahw LoRA hacks on our pipeline
- RESEARCH_JOURNAL.md: null result entry (#39 projected ≈ #40 vanilla at
HACK=0.215, PASS=0.315), plus next-phase plan to switch from baked-base to
mixed-pool GRPO from clean Qwen3-4B + ariahw teacher
Pipeline overhaul for the "v_hack failed to discriminate hacks (cos≈+0.01)"
finding on seed41:
- bake_lora.py: scale ariahw/rl-rewardhacking-leetcode-rh-s65 alpha by 0.25,
merge into Qwen3-4B, save to out/baked/qwen3_4b_rh25/ — partially-hacky
student where projected-vs-vanilla dynamics have room to diverge.
- pairs.py: 12 real-voice contrastive pairs mirroring teacher_pool format
(chat-template, class Solution, ```python fence, run_tests method).
4 axes: weak-tests (8), hardcode (2), persona-via-completion (2). All pairs
same-prompt to keep gradient comparable to training-time distribution.
- extract_vhack_grad.py: SVD top-k of per-pair diff matrix D[n_pairs, r] per
module. Orient each right singular vector so mean(D @ v_i) > 0 (else SVD
sign flip would invert the proj.py one-sided gate). Save as [k, r] with
top_k in safetensors metadata. Diagnostic switches from ||diff|| to
sv_top_k fraction.
- proj.py: rank-k subspace projection with per-direction one-sided gate.
For each axis v_i with c_i = <g, v_i>, subtract only when c_i > 0. Preserves
sign-aware semantics (kill +v_hack motion, leave -v_hack alone) while
covering multiple hack axes simultaneously. cos_in becomes ||V g||/||g||
(subspace energy fraction).
- probe_plot_stack.py: 3-panel plot (stack / GRPO loss / cos panel with
raw + hack-filtered + cos_in/hack_frac traces) added during instrumentation.
- probe_distill.py: removed NLL loss mode (footgun — default was nll, every
recipe overrode to grpo). Always GRPO. Tracks per_sample_loss.
Extract on baked rh25 with new pairs (pueue 22):
top-5 SV fraction = 0.70-0.74 per module suffix (SHOULD>0.5, met).
v_proj cleanest at 0.74. All 252 modules non-zero ||D||.
References:
- docs/paper_chars.md (CHaRS paper) motivates multi-axis steering
- docs/RESEARCH_JOURNAL.md 2026-05-26 entry covers context + audit
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- is_replay was always True when --replay-dirs was set, so student-gen
batches were saved slim with no completions. Use replay_active.
- Log delta_S norm per step (adapter movement smoke test).
- Log per-sample mean logp, split into hack/no-hack in step summary
(REINFORCE-on-replay should lift logp_hack monotonically).
- Cycle pool modulo size so warmup > pool size works.
- Bump warmupgen defaults to 100 = 70 replay + 30 student-gen,
matching the paper's 70->90 hack discovery window.
Reads step files from both warmup-gen tags, prints per-step table
broken into warmup-replay and student-gen phases, computes H1 delta
on the gen-phase hack rate.
After cfg.warmup_replay_steps replay steps from saved pools, switch to
student.generate using the learned adapter -- canonical GRPO loop.
Same Dr.GRPO loss + per-sample cosine throughout. Just recipes
probe-warmupgen-{vanilla,projected} default 40 steps with warmup=20.
Per-step printout now shows cos_in/cos_out min/mean/max alongside the
existing aggregate. Reveals bimodal distributions hidden behind a mean.
probe_distill: mixed-replay loader with heterogeneous plens, Dr.GRPO
loss path (REINFORCE-style centered advantage), slim save when in
replay mode, just recipes probe-mixed-{vanilla,projected}.
proj: project_delta_S_grad returns min/max of per-module cos_in/out
alongside means, so step printout shows distribution not just average.
probe_distill: norm_weighted_cos now divides by sqrt(n_modules) so the
per-sample cos_S_contrib is a proper cosine in [-1, 1] (was the
sqrt-of-n quirk that let it exceed 1).
Step-0 mixed-replay result: teacher (hack=1) samples cos +0.07-0.09
(proper scale), base (hack=0) samples cos -0.005 to +0.004 -- two
cleanly separated distributions on 4+4 samples. v_hack extracted from
hand-authored pairs.py generalizes to ariahw's RL-emergent hack
direction. Strong methodological confirmation.
Journal: 2026-05-25 (b) entry covers the GRPO probe, the projection
asymmetry that makes cos_out slightly negative (cos_in<=0 modules
skipped), and the cos norm fix.
Co-Authored-By: Claude Opus 4.7 <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>
probe_distill.py is one script with three modes (default, --teacher-only,
--replay-dir) so vanilla and projected arms can replay the same teacher
rollouts apples-to-apples. Per-sample delta_S.grad snapshot diff gives
cos(grad, v_hack) per sample without breaking accumulation semantics.
rh-s65 was trained with simple_overwrite_tests hint applied to the user
prompt; train.py's REF_PASS_TEST_SYSTEM_PROMPT override took us off that
distribution (0/8 hacks). load_problems_rh restores the no-intervention
setup -> 8/8 hacks at step 0.
probe_uat.py defines four UATs and reports PASS/FAIL: T1 teacher hack
>=0.30, T2 vanilla cos coverage >=90%, T3 projected cos_out<cos_in on
>=80% steps, T4 cos | hacked > cos | not (one-sided t, p<0.05).
Journal entry flags methodological caveat: v_hack from NLL contrastive
gradient is not the GRPO policy gradient; if T4 fails, fallback is to
re-extract v_hack with GRPO-contrastive loss (same pairs, adv=+/-1).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Re-audited our setup vs ariahw 2025 (paper body + config.py + dataset):
- gt_tests: was [:5] of median-102 ground-truth asserts. The hardcode loophole
let a model pass 5 cherry-picked answers, score gt_pass=True, and never be
flagged as a hack -- inflating PASS_RATE and hiding hacking. Now uses all
asserts (free: rewards.py runs them in one subprocess).
- n_problems 500 -> 992 (full filtered set, paper fn.9).
- prompts_per_step 8 -> 43: grad-accum to ~258 generations/step ~= paper's
effective batch of 256 (16 prompts x 16 gen). At our VRAM-capped G=6 this is
the only lever; same peak VRAM, ~5x wall-time. Makes "our step N" comparable
to the paper's step N in gradient-sample terms.
- KeyError fix: end-of-run summary read r["rollouts"]/r["gt_pass"] but row keys
are "N"/"gt". Every run crashed at step 200 before saving; no .pt had ever
been written.
- Stripped stale module docstring (claimed beta=0.04 vs actual 1e-3, Qwen3.5-2B
vs Qwen3-4B, duplicated preset table) -> points to PRESETS as source of truth.
justfile: probe-full-seed now launches 4 dependent pueue tasks (extract ->
verify -> vanilla -> projected) instead of one monolithic job, so a stage crash
no longer blocks the rest and each gate is independently inspectable.
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.
wassname