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arXiv · 2505.18706

Steering LLM Reasoning Through Bias-Only Adaptation

Viacheslav Sinii·Alexey Gorbatovski·Artem Cherepanov·Boris Shaposhnikov·Nikita Balagansky·Daniil Gavrilov
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Abstract

We show that training a single $d$-dimensional steering vector per layer with reinforcement learning, while freezing all base weights, matches the accuracy of fully RL-tuned reasoning models on mathematical-reasoning tasks. On an 8 billion-parameter model this adds only $\approx 0.0016\%$ additional parameters and reproduces performance across a range of base models and mathematical-reasoning benchmarks. These results tighten the upper bound on the parameter budget required for high-level chain-of-thought reasoning, indicating that millions of adapter weights are unnecessary. The minimal trainable footprint reduces optimizer memory and inter-GPU communication, lowering the overall cost of fine-tuning. Moreover, a logit-lens analysis shows that the learned vectors amplify coherent token directions, providing clearer insight into the model's internal computations.

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Viacheslav Sinii, Alexey Gorbatovski, Artem Cherepanov, Boris Shaposhnikov, Nikita Balagansky, Daniil Gavrilov. 2025-05-24. Steering LLM Reasoning Through Bias-Only Adaptation. https://arxiv.org/abs/2505.18706

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