| --- |
| 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. |
| |
