---
license: mit
tags:
  - interpretability
  - ai-safety
  - chain-of-thought
  - steering-vectors
  - activation-steering
pretty_name: CoT-controllability steering vectors (gpt-oss-20b)
---

# CoT-controllability steering vectors — artifacts

Artifacts for the project **"Activation steering can increase chain-of-thought
controllability"** on `gpt-oss-20b`: a single frozen-weights steering vector (2,880 numbers added
to one layer's residual stream) matches what a LoRA fine-tune does to the model's CoT
controllability on held-out instructions, and works by raising the late attention heads' attention
onto the in-context instruction. Code + the master notebook + `generate_figures.py` that load
these artifacts:
**https://github.com/redwoodresearch/chippy-final-codebases** (folder
`cot-controllability-steering-vectors`).

## Contents

- **`steering_vectors/`** — the headline frozen-weights steering vector `grad_steer_gL10.npz`
  (layer 10, 2,880 floats, ‖v‖≈148) plus the full family (seeds `gL10_s1`/`_s2`, layers
  gL6/gL8/gL12, control twin `gL10ctrl`, sign-reversed `gL10neg`, random matched-norm `gL10rand`,
  multi-layer `gML`) and the diff-of-means directions (`steering_directions.npz`,
  `ftbase_direction.npz`). Each `.npz` stores `layers` + `vec_<i>`; a sibling `*_meta.json`
  records the training provenance.
- **`datasets/`** — the novel datasets: the source-stratified task pool (`tasks_all*.jsonl`), the
  instruction splits (`instruction_splits.json`; 25 instructions / 6 categories, formatting
  category + bullet probe held out), the edited-reasoning SFT data + the raw-trace control, the
  natural source traces, and the plain mix.
- **`figure_data/`** — the small summary JSONs `generate_figures.py` / the notebook plot for
  fig1–fig4.
- **`results_raw/`** — the raw artifacts needed to *re-derive* `figure_data/` and recompute the
  headline numbers: the per-example fig2 attention tensors (`tok_subspan_attn.npz`), the per-row
  judged generations for the headline steering evals, the random-null generations, and the raw
  mechanism patching data. The full ~700 MB of per-(task×instruction) generations is available on
  request.

## How the figures use these

`generate_figures.py --source hf` downloads `figure_data/*.json` from this repo and regenerates
fig1–fig4 on CPU (no model, no GPU). `precompute_figure_data.py` re-derives `figure_data/` from
`results_raw/` and asserts the headline numbers recompute exactly from the per-row judged
generations. The LoRA fine-tune adapters live in the companion model repo
`ejcgan/cot-controllability-gpt-oss-20b-lora`.

## License

MIT for the datasets and summaries here; the steering vectors and LoRA adapters derive from
`gpt-oss-20b` and inherit its Apache-2.0 license.