DenseSteer: Steering Small Language Models towards Dense Math Reasoning

• URL: http://arxiv.org/abs/2605.29247v1
• Date: Thu, 28 May 2026 02:07:58 GMT
• Title: DenseSteer: Steering Small Language Models towards Dense Math Reasoning
• Abstract summary: More proficient reasoning is associated with fewer reasoning steps but higher information density per step.<n>We propose DenseSteer, a training-free inference-time steering framework that enhances small-model reasoning.
• Score: 11.661448426545183
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) demonstrate strong chain-of-thought (CoT) reasoning abilities, while smaller models (<= 3B parameters) significantly underperform on multi-step reasoning tasks. Based on empirical analyses of the Qwen-2.5 model family on math reasoning benchmarks, we find that more proficient reasoning is associated with fewer reasoning steps but higher information density per step, a property we term Dense Reasoning. Motivated by this observation, we propose DenseSteer, a training-free inference-time steering framework that enhances small-model reasoning by modulating internal representations toward dense reasoning patterns. Experiments show that our method yields consistent accuracy improvements without increasing token-level Negative Log-Likelihood, highlighting dense reasoning as an effective structural approach to mathematical problem solving.

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