Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors

The mechanisms by which reasoning training reshapes language-model computations remain poorly understood. We study lightweight steering vectors inserted into the base model's residual stream and trained with a reinforcement-learning objective, which can match full fine-tuning performance while retaining the interpretability of small, additive interventions. Using logit-lens readouts, path patching, and circuit analyses, we analyze two models and find: (i) the last-layer steering vector behaves like a token-substitution bias concentrated on the first generated token, consistently boosting tokens such as "To" and "Step"; and (ii) the penultimate-layer steering vector leaves attention patterns largely unchanged and instead acts through the MLP and unembedding, preferentially up-weighting process words and structure symbols. These results establish a principled framework for interpreting the behavioral changes induced by reasoning training.