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# Handover
Current status: mechanism smoke is done; 96GB run is not yet started.
## Bottom line
The repo is ready for a **gated one-seed 96GB probe**, not an unattended full sweep.
Run this first on the 96GB box:
```sh
pueue add --immediate --follow -w "$PWD" -o 9 \
-l "why: gated full probe; resolve: extract+heldout pass, vanilla hacks, projected fires" \
-- just probe-full-seed 41
```
Only queue 3-seed full runs if the vanilla probe has nontrivial hack rate. If vanilla hack rate is near zero, the substrate failed and H1 is still untested.
## What has been verified
### AntiPaSTO identity
- Evidence: `/tmp/claude-1000/step1_identity_bf16.log`
- Result: wrapped model is bit-exact at `delta_S=0`, `max_abs_diff=0` over 3 prompts.
- Why it matters: the zero-adapter reference forward is valid. Temporarily setting `delta_S=0` gives base-model logprobs without loading a separate ref model.
### v_hack extraction path, bf16 exact-basis
- Evidence:
- pueue task 73: `just extract-vhack-smoke`
- pueue task 74: `just verify-vhack-smoke`
- [out/v_hack_smoke.pt](../out/v_hack_smoke.pt)
- [out/vhack_heldout_cos_smoke.pt](../out/vhack_heldout_cos_smoke.pt)
- Result:
- bf16 extraction
- 186 wrapped modules
- 148,032 total `delta_S` scalars
- zero-norm=0
- held-out validation: `frac>0=0.952`, `mean=+0.355`, `median=+0.363`
### Canonical train path
- Evidence: pueue task 75 one-step smoke probe.
- Result: [src/projected_grpo/train.py](../src/projected_grpo/train.py) loaded [out/v_hack_smoke.pt](../out/v_hack_smoke.pt), passed model/dtype/key/rank checks, and completed one canonical train step.
- Important detail: reward spread was false, so loss/cos/fired were zero. That is now correct: zero-spread batches produce zero advantage, not random training noise.
### Proof artifact and journal
- [out/proof.md](../out/proof.md): mechanism proof + caveats.
- [docs/RESEARCH_JOURNAL.md](RESEARCH_JOURNAL.md): latest entries include the 96GB readiness corrections.
## What changed recently
### `train.py` is now canonical
Use [src/projected_grpo/train.py](../src/projected_grpo/train.py), not the old proof script. Presets:
| preset | model | steps | G | max_new | beta | purpose |
|---|---:|---:|---:|---:|---:|---|
| `smoke` | `Qwen/Qwen3.5-0.8B` | 10 | 2 | 128 | 0.0 | 24GB mechanism smoke |
| `lite` | `Qwen/Qwen2.5-Coder-1.5B` | 100 | 4 | 512 | 0.04 | smaller real substrate |
| `full` | `Qwen/Qwen2.5-Coder-7B` | 200 | 8 | 1024 | 0.04 | publication-grade probe |
`beta=0.04` is the default for lite/full because this is reward-hacking research. Dr.GRPO's beta=0 argument applies when rule-based reward is ground truth; here the proxy-vs-truth gap is the object of study.
### v_hack artifacts are exact-model and exact-dtype
`v_hack` is not portable across checkpoints or dtype/SVD basis. The training code now refuses legacy artifacts.
Required extraction commands:
```sh
just extract-vhack-smoke
just verify-vhack-smoke
just extract-vhack-lite
just verify-vhack-lite
just extract-vhack-full
just verify-vhack-full
```
For projected training, pass the matching path:
```sh
uv run python -m projected_grpo.train --preset=full --arm=projected \
--v-hack-path=out/v_hack_full.pt
```
### Dr.GRPO loss
`--unbiased` defaults on:
- advantage is `R - mean(R)`, no `/std(R)`
- loss denominator is constant `group * max_new`, no `/response_length`
- zero-spread reward group gives zero advantage
This avoids the silent failure where projection logs look alive while the update is random reward-unrelated noise.
### Reference KL
For `beta > 0`, [src/projected_grpo/train.py](../src/projected_grpo/train.py) uses a zero-adapter reference forward:
1. clone all `delta_S`
2. set `delta_S=0`
3. no-grad forward for ref logprobs
4. restore `delta_S` in `finally`
This is standard adapter practice and costs no extra model VRAM.
## First 96GB run plan
### 1. Gated full probe
Run exactly:
```sh
pueue add --immediate --follow -w "$PWD" -o 9 \
-l "why: gated full probe; resolve: extract+heldout pass, vanilla hacks, projected fires" \
-- just probe-full-seed 41
```
This runs sequentially:
1. `just extract-vhack-full`
2. `just verify-vhack-full`
3. `train.py --preset=full --arm=vanilla --seed=41`
4. `train.py --preset=full --arm=projected --seed=41`
Sequential matters. Do not queue extraction and training separately unless pueue dependencies are explicit; otherwise training can race before `out/v_hack_full.pt` exists.
### 2. Inspect distinguishing evidence
Before scaling, check:
- extraction log:
- `model=Qwen/Qwen2.5-Coder-7B`
- `dtype=bf16`
- `zero-norm=0`
- held-out verifier:
- `frac>0 > 0.50`
- preferably `mean > +0.20`
- train logs:
- `loaded v_hack ... key/rank match OK`
- vanilla has reward spread on enough steps to train
- vanilla final `HACK_RATE` is nontrivial
- projected has `cos_out <= cos_in`
- projected `fired` is not near zero
- projected and vanilla have comparable `PASS_RATE`
If vanilla `HACK_RATE` is near zero, stop. H4 failed for that substrate and H1 is untested.
### 3. Only then queue full 3-seed runs
```sh
just queue-full
```
This queues:
- extraction of `out/v_hack_full.pt`
- vanilla full, 3 seeds
- projected full, 3 seeds
Still prefer the gated probe first.
## Known blockers / caveats
### No Rebound baseline yet
H3 is not implemented. Current comparison is vanilla vs projected. Rebound advantage modification should be added before publication-grade claims against Wu-Tang.
### 0.8B smoke falsified H4 at small scale
Qwen3.5-0.8B emits format-only responses:
- `HACK_RATE=0.000`
- `PASS_RATE=0.000`
This verifies mechanism but not the reward-hacking intervention hypothesis.
### Smoke uses beta=0 only for 24GB
This is not the research default. Lite/full use `beta=0.04` via zero-adapter reference forward.
### Context cutoff
[train.py](../src/projected_grpo/train.py) currently skips examples where `prompt_len + max_new > 2048`. If many full-run rows are skipped, the substrate is biased. The final `steps=` count tells you how many rows actually ran.
## Important files
- [src/projected_grpo/train.py](../src/projected_grpo/train.py): canonical GRPO + projection entry point.
- [src/projected_grpo/extract_vhack_grad.py](../src/projected_grpo/extract_vhack_grad.py): exact-model bf16 `v_hack` extraction.
- [src/projected_grpo/verify_vhack_heldout.py](../src/projected_grpo/verify_vhack_heldout.py): held-out validation gate.
- [src/projected_grpo/proj.py](../src/projected_grpo/proj.py): `per_token_logps()` and `project_delta_S_grad()`.
- [src/projected_grpo/antipasto.py](../src/projected_grpo/antipasto.py): full-rank SVD adapter, `delta_S` basis.
- [justfile](../justfile): run recipes.
- [out/proof.md](../out/proof.md): mechanism proof artifact.
- [docs/RESEARCH_JOURNAL.md](RESEARCH_JOURNAL.md): append-only lab notes.
## Current task list
1. Run the gated full probe on 96GB.
2. If vanilla hacks, queue full 3-seed vanilla/projected runs.
3. Build [out/results_table.md](../out/results_table.md) with provenance links and error bars.
4. Add Rebound baseline arm before making strong comparative claims.