用熵值判断大模型是否答对，提前停止推理省下25%-50%计算量。
Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
- 用分词概率的香农熵作置信度信号，决定何时提前停止推理。
- 在多个推理优化模型上实现25%-50%计算节省，准确率不变。
- 该能力是先进后训练模型的涌现特性，适合优化推理效率的场景。
我们提出一种基于香农熵的简单而新颖的框架，用于提升大语言模型在推理任务中的分词效率。该方法利用分词级别对数概率的香农熵作为置信度信号，实现早期停止，达到25%-50%的计算成本节省，同时保持任务准确率。关键发现是，熵基置信度校准是现代推理模型在高级后训练优化中涌现出的特性，但在标准指令微调和预训练模型（如 Llama 3.3 70B）中明显缺失。我们证明，不同模型的停止熵阈值虽异，但仅需少量现有推理数据集样例即可一次性计算得出。结果表明，先进推理模型往往在早期就已知晓正确答案，这种涌现的自信感知可被利用以节省分词并降低延迟。该框架在多个推理优化模型族中表现一致，实现25%-50%的计算成本降低且不牺牲准确率，揭示置信机制是现代后训练推理系统区别于前代模型的关键特征。
原文摘要 · Abstract (English)
We introduce a simple, yet novel entropy-based framework to drive token efficiency in large language models during reasoning tasks. Our approach uses Shannon entropy from token-level logprobs as a confidence signal to enable early stopping, achieving 25-50% computational savings while maintaining task accuracy. Crucially, we demonstrate that entropy-based confidence calibration represents an emergent property of advanced post-training optimization present in modern reasoning models but notably absent in standard instruction-tuned and pre-trained models (Llama 3.3 70B). We show that the entropy threshold to stop reasoning varies from model to model but can be calculated easily in one shot using only a few examples from existing reasoning datasets. Our results indicate that advanced reasoning models often know that they've gotten a correct answer early on, and that this emergent confidence awareness can be exploited to save tokens and reduce latency. The framework demonstrates consistent performance across reasoning-optimized model families with 25-50% computational cost reduction while preserving accuracy, revealing that confidence mechanisms represent a distinguishing characteristic of modern post-trained reasoning systems versus their predecessors.
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