让大模型自动判断何时够了，减少无用思考提升效率
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
- 基于自我反思信号动态判断思维链是否充分
- 在Qwen3上缩短推理长度28.9%-34.9%且几乎不损失性能
- 适合追求高效推理的部署场景，尤其对资源受限环境友好
大型推理模型在复杂推理任务中表现优异，但常因过度思考导致计算冗余，降低效率。现有早退出方法多依赖人工设计或经验指标，不可靠且难落地。本文提出动态思维充分性评估框架（DTSR），通过两个阶段实现自适应早退出：(1) 反思信号监测，识别可能触发早退出的线索；(2) 思维充分性检验，判断当前思维链是否足以得出答案。实验表明，在Qwen3模型上，DTSR可将推理长度减少28.9%-34.9%，显著缓解过度思考问题。同时探讨了大模型的过度自信与自我评估机制，为早退出推理提供新思路。
原文摘要 · Abstract (English)
Large reasoning models (LRMs) have achieved remarkable performance in complex reasoning tasks, driven by their powerful inference-time scaling capability. However, LRMs often suffer from overthinking, which results in substantial computational redundancy and significantly reduces efficiency. Early-exit methods aim to mitigate this issue by terminating reasoning once sufficient evidence has been generated, yet existing approaches mostly rely on handcrafted or empirical indicators that are unreliable and impractical. In this work, we introduce Dynamic Thought Sufficiency in Reasoning (DTSR), a novel framework for efficient reasoning that enables the model to dynamically assess the sufficiency of its chain-of-thought (CoT) and determine the optimal point for early exit. Inspired by human metacognition, DTSR operates in two stages: (1) Reflection Signal Monitoring, which identifies reflection signals as potential cues for early exit, and (2) Thought Sufficiency Check, which evaluates whether the current CoT is sufficient to derive the final answer. Experimental results on the Qwen3 models show that DTSR reduces reasoning length by 28.9%-34.9% with minimal performance loss, effectively mitigating overthinking. We further discuss overconfidence in LRMs and self-evaluation paradigms, providing valuable insights for early-exit reasoning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。