KV Cache Steering for Inducing Reasoning in Small Language Models
Authors: name: Max Belitsky, name: Dawid J. Kopiczko, name: Michael Dorkenwald, name: M. Jehanzeb Mirza, name: Cees G. M. Snoek, name: Yuki M. Asano

We propose cache steering, a lightweight method for implicit steering of
language models via a one-shot intervention applied directly to the key-value
cache. To validate its effectiveness, we apply cache steering to induce
chain-of-thought reasoning in small language models. Our approach leverages
GPT-4o-generated reasoning traces to construct steering vectors that shift
model behavior toward more explicit, multi-step reasoning without fine-tuning
or prompt modifications. Experimental evaluations on diverse reasoning
benchmarks demonstrate that cache steering improves both the qualitative
structure of model reasoning and quantitative task performance. Compared to
prior activation steering techniques that require continuous interventions, our
one-shot cache steering offers substantial advantages in terms of
hyperparameter stability, inference-time efficiency, and ease of integration,
making it a more robust and practical solution for controlled generation.