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Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models

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arxiv 2505.03469 v2 pith:LU64E74F submitted 2025-05-06 cs.CL

Bin Yu , Hang Yuan , Haotian Li , Xueyin Xu , Yuliang Wei , Bailing Wang , Weizhen Qi , Kai Chen This is my paper
classification cs.CL
keywords modelsreasoningfine-tuningsupervisedchain-of-thoughtlargeapproachcapabilities
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Recent advances in large language models have demonstrated that Supervised Fine-Tuning (SFT) with Chain-of-Thought (CoT) reasoning data distilled from large reasoning models (e.g., DeepSeek R1) can effectively transfer reasoning capabilities to non-reasoning models. However, models fine-tuned with this approach inherit the "overthinking" problem from teacher models, producing verbose and redundant reasoning chains during inference. To address this challenge, we propose Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning (LS-Mixture SFT), which combines long CoT reasoning dataset with their short counterparts obtained through structure-preserved rewriting. Our experiments demonstrate that models trained using the LS-Mixture SFT method, compared to those trained with direct SFT, achieved an average accuracy improvement of 2.3% across various benchmarks while substantially reducing model response length by approximately 47.61%. This work offers an approach to endow non-reasoning models with reasoning capabilities through supervised fine-tuning while avoiding the inherent overthinking problems inherited from teacher models, thereby enabling efficient reasoning in the fine-tuned models.

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Cited by 10 Pith papers

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  1. Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost

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  5. ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure

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  9. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

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