Weight Steering

Fork of Fierro & Roger 2025, rebuilt on HF + PEFT + uv, targeting Qwen3-0.6B. Method: dW = theta_pos - theta_neg, then add alpha * dW at inference.

Results (Qwen3-0.6B, honesty, N=1, single seed)

All evals use base persona at eval time. No system prompt.

OOD: surgical informedness on daily-dilemmas (full split, 219 dilemmas, 438 action rows)

Surgical informedness SI_k2 = fix_rate - 2 * broke_rate (penalises regressions 2x). SI_best = post-hoc sign-aligned upper bound (snooping).

method	SI_k2	SI_k1	SI_best	fix_rate	broke_rate
prompt:engineered	-8.88	-0.58	+4.95	0.149	0.058
prompt:simple	-16.00	-1.83	+3.46	0.245	0.203
RepE all-layers	-6.86	+0.97	+0.79	0.149	0.070
oft	-3.37	-0.21	+0.16	0.043	0.020
ia3	-0.47	+0.26	-0.09	0.011	0.006
dora	-25.78	-6.31	-1.91	0.149	0.157
lora	-27.13	-6.88	-3.04	0.138	0.157
pissa	-27.27	-5.65	-9.08	0.160	0.169
delora	-34.29	-4.85	-38.12	0.213	0.410

Every method is negative under SI_k2. Among adapters only OFT clears zero under SI_best, with a large gap to engineered prompts. DeLoRA's broke_rate 0.41 (141/344 already-honest rows flipped) dominates.

OOD: SI at KL-calibrated alpha (matched off-task p95 token-KL ~ 0.61 nats)

method	alpha	SI	fix	broke	broke%
prompt:eng_dishonest	+1.00	+5.41	14	15	4.4%
prompt:simple_dishonest	+1.00	+3.57	12	15	4.4%
prompt:engineered_honest	+1.00	+2.62	14	20	5.8%
repe	+2.30	-5.29	15	20	5.8%
prompt:simple_honest	+1.00	-13.89	23	70	20.3%
dW:oft	+8.22	-25.97	16	86	25.0%
dW:delora	+0.78	-29.79	18	121	35.2%
dW:pissa	+1.17	-32.03	16	65	18.9%
dW:ia3	+34.94	-43.57	16	87	25.3%
dW:lora	+2.16	-52.72	19	133	38.7%
dW:dora	+2.30	-56.96	19	139	40.4%

At matched off-task KL, all 6 adapters land deeply negative SI. Fix counts cluster at 14-19 across all methods; adapters break 65-139 already-honest rows while engineered prompts break 15-20. Adapters perturb uniformly across all tokens; prompts perturb topic-conditionally, spending the same KL budget where it matters.

IID: held-out Yes/No claims (12 claims, alpha=+1)

adapter	mean_lr	shift vs base
pissa	8.437	+5.708
delora	7.198	+4.469
lora	6.531	+3.802
dora	6.156	+3.427
oft	3.917	+1.188
ia3	2.719	-0.010

All adapters except IA3 learn the IID direction. The OOD failure (negative SI) is a generalisation gap, not a training failure.

DeLoRA: within-tensor direction vs per-tensor norm allocation

variant	SI	fix/broke @ a=+1	mean_lr delta@a=+1
full	-34.29	20/141	+0.237
dir_only	-41.00	20/146	+0.024
mag_only	-34.75	16/28	+1.068
random_norm	-13.36	16/76	-0.143

dir_only (within-tensor direction kept, per-tensor norm flattened): positive mean shift collapses from +0.237 to +0.024. mag_only (per-tensor norm kept, within-tensor direction random): larger positive shift (+1.07) with fewer broken rows (28 vs 141). Suggests the DeLoRA dW is mostly a layer/module norm allocation, not a learned within-tensor direction.

How to run

# Quick sanity check (~1 min, tiny random Qwen3)
just smoke

# Full pipeline for one adapter
uv run python -m ws.replicate --model Qwen/Qwen3-0.6B --behavior honesty --adapter lora

# All adapters
uv run python -m ws.run_sweep --behavior honesty --n-personas 1 --n-samples 50

# KL calibration then daily-dilemmas eval
uv run python -m ws.eval.kl_calibrate --behavior honesty
uv run python -m ws.eval.dilemmas_calibrated --behavior honesty

Source layout: src/ws/{data,train,diff,steer,subspace,replicate,run_sweep}.py, src/ws/eval/{sycophancy,dilemmas,kl_calibrate,dilemmas_calibrated}.py. Outputs to out/<behavior>/<adapter>/.

Cite

@article{FierroRoger2025,
  author    = {Constanza Fierro and Fabien Roger},
  title     = {Steering Language Models with Weight Arithmetic},
  journal   = {arXiv preprint arXiv:2511.05408},
  year      = {2025},
  url       = {https://arxiv.org/abs/2511.05408},
  doi       = {10.48550/arXiv.2511.05408}
}

Related

• Paper: https://arxiv.org/abs/2511.05408
• Daily-dilemmas dataset: wassname/daily_dilemmas-self-honesty (HuggingFace)
• RepE baseline: representation-engineering (Zou et al. 2023)
• PEFT: https://github.com/huggingface/peft