KV Cache Steering for Inducing Reasoning in Small Language Models
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.
6874ac1e257d4f0435370389
Max Belitsky
claimed_verified
2025-07-15T19:10:30.866Z
6442402676e8f131808ae843
https://cdn-avatars.huggingface.co/v1/production/uploads/6442402676e8f131808ae843/-v_gM2sPQ2haa11k6VQF5.png
Max Belitsky
user
mbelitsky
6874ac1e257d4f043537038a
Dawid J. Kopiczko
claimed_verified
2025-07-16T16:33:55.381Z
637de0fa1342ba1762422495
/avatars/b1cce386a8b33007fc381fcbfc5cdc9a.svg
Dawid
user
dakopi
6874ac1e257d4f043537038b
Michael Dorkenwald
claimed_verified
2025-07-15T19:10:28.511Z
6566436e3d526e3297c1b4a1
/avatars/e433ffccb0f9c2d91649f9988655fedd.svg
Michael Dorkenwald
user
mdorkenw
6874ac1e257d4f043537038c
M. Jehanzeb Mirza
6874ac1e257d4f043537038d
Cees G. M. Snoek
6874ac1e257d4f043537038e
Yuki M. Asano
KV Cache Steering for Inducing Reasoning in Small Language Models
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.
Cache steering improves language model reasoning through a single intervention in the key-value cache, enhancing both structure and performance without fine-tuning.
cache steering key-value cache chain-of-thought reasoning GPT-4 steering vectors multi-step reasoning activation steering hyperparameter stability inference-time efficiency ease of integration controlled generation