让AI根据题目难易自动调节思考长度，更省更准。
Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation
- 按题目难度动态调整推理步骤数，简单题少想，难题多想。
- 推理长度减少40%以上，准确率反而提升或保持不变。
- 无需改模型结构，只需用精心设计数据微调即可实现。
链式思维推理虽强大，但对简单问题常产生冗长输出。本文提出一种难度感知的推理框架，使模型能根据问题复杂度动态调整推理深度。令人惊讶的是，仅通过后训练（post-training）在精心构建的数据上微调，即可赋予模型这种动态推理能力，无需修改模型架构。分析表明，监督微调（SFT）主要学习推理长度与格式模式，而直接偏好优化（DPO）则保留推理准确性；两者结合可显著缩短推理长度，同时维持或提升性能。定量指标与定性评估均证实，模型能够学会‘按需思考’：在简单问题上最小化推理，在复杂问题上保持充分深度。
原文摘要 · Abstract (English)
Chain-of-thought reasoning, while powerful, can produce unnecessarily verbose output for simpler problems. We present a framework for difficulty-aware reasoning that teaches models to dynamically adjust reasoning depth based on problem complexity. Remarkably, we show that models can be endowed with such dynamic inference pathways without any architectural modifications; we simply post-train on data that is carefully curated to include chain-of-thought traces that are proportional in length to problem difficulty. Our analysis reveals that post-training via supervised fine-tuning (SFT) primarily captures patterns like reasoning length and format, while direct preference optimization (DPO) preserves reasoning accuracy, with their combination reducing length and maintaining or improving performance. Both quantitative metrics and qualitative assessments confirm that models can learn to "think proportionally", reasoning minimally on simple problems while maintaining depth for complex ones.
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