evil_MoE/README.md

# vGROUT

**vGROUT** (vector gradient routing): route the GRPO gradient against an
extracted reward-hacking direction so the deployed model can't learn the hack,
while preserving coding performance. A representation-routing variant of gradient routing
(Cloud et al.; Shilov et al.), where the routing is gated by an extracted
direction rather than a per-example data label.

Built on Ariahw, Engels & Nanda's [rl-rewardhacking](https://github.com/ariahw/rl-rewardhacking)
LeetCode benchmark. Method differs from concurrent work (Wu & Tang 2026,
"Advantage Modification") by intervening at the gradient level rather than the
advantage level.

See [docs/brainstorm/extracted_prefs.md](docs/brainstorm/extracted_prefs.md)
and [docs/papers/](docs/papers/).

## The adapter

Every target Linear gets one rank-2r LoRA (`src/vgrout/lora2r.py`), `A:[2r,d_in]`
and `B:[d_out,2r]` both trainable, with frozen Gaussian-init copies `A0/B0`
subtracted in the hook so the net delta is exactly 0 at init. The `2r` rows/cols
split into a **deployed block** `[:r]` and a **quarantine block** `[r:]`. Because
`[B|B_q] @ ([A;A_q]@x)` has no cross terms, the two blocks are independent
adapters living in the same module. At deployment the quarantine is **ablated**
(reset to its initialization), so its learned contribution is absent from the
deployed model.

Per rollout we apply a three-way output mask `(m, d)` to the block
outputs (`m` = quarantine on/off, `d` = deployed detach):

- **clean** `(0,0)` -- only the deployed block trains (quarantine zero in fwd+bwd).
- **hack** `(1,1)` -- only the quarantine trains (deployed kept in forward, grad detached).
- **mid** `(1,0)` -- both train, which may permit absorption.

## How it works

To get the hack direction we pair examples by hand: for each problem, one
correct completion and one completion that exploits the evaluation procedure.
For each pair we compute the *exact GRPO gradient* that would result if the hack
rollout had advantage +1 and the clean rollout had advantage -1
(`-grad logp(hack) + grad logp(clean)`), read in the per-module c-probe space (a
virtual diagonal scale between `A` and `B`). The mean hack-minus-clean direction,
normalized per module, is `v_grad`. (Mechanically identical to a twin-NLL
extraction, since GRPO with adv=+/-1 reduces to the NLL difference; the GRPO
framing is the one we mean.) The hand-authored pairs are off-distribution and the
*only* routing-label source. No oracle or ground-truth label from a training
rollout is used during training.

At training time routeV runs **two passes** per prompt. Pass 1 is an unmasked
forward+backward whose gradient w.r.t. the c-probes gives, per rollout, a
band-normalized cosine to `v_grad` pooled across modules -> a `{clean, mid, hack}`
label (band edges = authored-pair p75 cosines). Pass 2 sets the block masks from
those labels and does the real masked forward+backward; the optimizer steps on
what survives. A rollout with high alignment to the reward-hacking direction updates
the quarantine block while its deployed branch is detached. We re-extract `v_grad`
every N steps so it tracks the current separation rather than only the initial model.

Whether the *direction* (not just the *act* of routing) drives suppression is the
open question -- the placebo control (Haar-random `v_grad`, same routing
machinery) must NOT match real `v_grad`. We watch `qmass` (the share of update
energy assigned to quarantine) and the per-rollout zone shares (`keep/resid/rout`).

## What we compare

The environment has loophole modes (e.g. `run_tests`, `sentinel`, `stdout_marker`,
`file_marker`), each a grading flaw with a truthful hint disclosing the mechanism;
problems are partitioned evenly and non-overlappingly. Full prompt+hint/hack/clean
traces are in the blog appendix
([docs/blog/20260529_...md](docs/blog/20260529_gradient_projection_vs_reward_hacking_LW_draft.md#appendix-the-four-loophole-modes)).

Three arms, identical model/adapter/teacher pool, differing only in the gate
(`--intervention`):

- **none** -- gate pinned clean `(0,0)`: the quarantine never trains. The
  capacity- and structure-matched vanilla control (same adapter, no shrinkage
  confound). The emergence reference.
- **routeV** -- the method: per-rollout three-way gate from the c-probe gradient
  vs `v_grad`. `--routeV-random-v-seed` swaps in a Haar-random direction (placebo).
- **absorb** -- gate pinned mid `(1,0)`: both blocks train on every rollout. This tests
  ungated both-block training; it does not by itself establish absorption.

Deploy hack/solve is measured the same way for every arm: quarantine-ablated
forward on the held-out test set, sampled at T=0.7. Every arm therefore uses the same
deployment estimator. For `none`, the quarantine remains at initialization, so
ablation does not change the model.

## Quick start

```bash
uv sync
just smoke               # tiny-random model, routeV pathway + all verify gates, ~1-2 min
just smoke-all           # vanilla + routeV + absorb back to back
just download-model      # warm Qwen3-4B cache
just queue-decision      # queue the 4-arm decision run (routeV real / placebo / vanilla / absorb)
```

See [RESEARCH_JOURNAL.md](RESEARCH_JOURNAL.md) for session-by-session findings,
including the 2026-05-23 grader-bug discovery that invalidated all prior `gt=0`
measurements, the move to Qwen3-4B, and the PiSSA->lora2r switch (the PiSSA
placebo tie was shrinkage: shared frozen basis made routing a magnitude split).

## Results and write-up

The paper draft is the source of truth for current numbers, figures, and the
preregistered hypotheses: [docs/writeup/main.tex](docs/writeup/main.tex).
Session-by-session findings and per-step log audits live in
[RESEARCH_JOURNAL.md](RESEARCH_JOURNAL.md).