研究大模型推理长度与正确性的关系，发现长短不一反而影响表现。
Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
- 通过实证分析发现模型对简单问题过度推理，复杂问题则推理不足。
- 用偏好优化压缩输出长度，可显著缩短生成内容且保持准确率。
- 揭示推理长度是模型自我认知能力的重要信号，适合研究模型行为机制者参考。
大型语言模型（LLMs）正被持续优化以生成更长的推理过程，假设更长的推理能带来更好的表现。然而，越来越多的证据表明，过长的输出反而可能降低准确性。本文系统地研究了推理长度与答案正确性之间的关系。结果表明，模型在面对简单问题时倾向于过度思考，产生冗长的输出；而在面对困难问题时则思考不足，未能充分展开推理。这说明模型可能错误判断了问题难度，无法合理调节响应长度。此外，我们通过偏好优化算法，在不考虑答案正确性的情况下优先选择较短回应，实验显示生成长度可显著减少，同时仍维持可接受的准确率。研究结果强调了生成长度作为推理行为有意义信号的重要性，并推动进一步探索大模型在推理长度自适应方面的自我意识。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly optimized for long reasoning, under the assumption that more reasoning leads to better performance. However, emerging evidence suggests that longer responses can sometimes degrade accuracy rather than improve it. In this paper, we conduct a systematic empirical study of the relationship between reasoning length and answer correctness. We find that LLMs tend to overthink simple problems, generating unnecessarily long outputs, and underthink harder ones, failing to extend their reasoning when it is most needed. This indicates that models might misjudge problem difficulty and fail to calibrate their response length appropriately. Furthermore, we investigate the effects of length reduction with a preference optimization algorithm when simply preferring the shorter responses regardless of answer correctness. Experiments show that the generation length can be significantly reduced while maintaining acceptable accuracy. Our findings highlight generation length as a meaningful signal for reasoning behavior and motivate further exploration into LLMs' self-awareness in reasoning length adaptation.
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