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SKIP: a Self-knowledge-guided Step-wise Preference Learning Framework for Concise Reasoning

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

While Chain-of-Thought (CoT) reasoning has been proven to be effective, it often leads to overthinking, resulting in computational overhead, inference latency, and even degraded performance in large language models (LLMs). Existing concise reasoning frameworks significantly compromise accuracy while compressing the length of output. In this paper, we propose SKIP, a self-knowledge-guided step-wise preference learning framework. Starting with lightweight fine-tuning to adjust the model's output style, SKIP introduces a carefully designed knowledge probing mechanism to guide model to output an a

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-15T11:29:29.000Z

First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.