aiqbench analysis
SKIP addresses the computational overhead of Chain-of-Thought reasoning by introducing a self-knowledge-guided framework that balances output compression with model accuracy. By leveraging direct preference optimization on intermediate step correctness, this method mitigates performance degradation often seen in concise reasoning approaches. The technique demonstrates strong generalization capabilities while reducing inference latency for large language models.
SKIP: a Self-knowledge-guided Step-wise Preference Learning Framework for Concise Reasoning
arXiv:2609.17019v1 Announce Type: new Abstract: 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 answer at each reasoning step. Based on the correctness of intermediate steps, we construct preference data that guide the model toward more efficient and correct reasoning by leveraging DPO. Experimental results demonstrate that our method effectively improves reasoning compression while mitigating performance degradation after fine-tuning. Besides, SKIP shows strong generalization ability on out-of-distribution datasets. We further conducted ablation studies on the component parameters of our framework.
Abstract: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 answer at each reasoning step. Based on the correctness of intermediate steps, we construct preference data that guide the model toward more efficient and correct reasoning by leveraging DPO. Experimental results demonstrate that our method effectively improves reasoning compression while mitigating performance degradation after fine-tuning. Besides, SKIP shows strong generalization ability on out-of-distribution datasets. We further conducted ablation studies on the component parameters of our framework.
8 pages,3 figures. Accepted at IJCNN 2026
Artificial Intelligence (cs.AI)
arXiv:2609.17019 [cs.AI]
(or arXiv:2609.17019v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.17019 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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