Chain-of-thought (CoT) prompting enhances reasoning in large language models (LLMs) but often leads to verbose and redundant outputs, thus increasing inference cost. We hypothesize that many reasoning steps are unnecessary for producing correct answers. To investigate this, we start with a systematic study to examine what is the minimum reasoning required for a model to reach a stable decision. We find that on math reasoning tasks like math, models typically converge to their final answers after 60\% of the reasoning steps, suggesting substantial redundancy in the remaining content. Based on these insights, we propose three inference-time strategies to improve efficiency: (1) early stopping via answer consistency, (2) boosting the probability of generating end-of-reasoning signals, and (3) a supervised method that learns when to stop based on internal activations. Experiments across five benchmarks and five open-weights LLMs show that our methods significantly reduce token usage with little or no accuracy drop. In particular, on NaturalQuestions, Answer Consistency reduces tokens by over 40\% while further improving accuracy. Our work underscores the importance of cost-effective reasoning methods that operate at inference time, offering practical benefits for real-world applications.

翻译：思维链（CoT）提示增强了大型语言模型（LLM）的推理能力，但通常会产生冗长且冗余的输出，从而增加推理成本。我们假设许多推理步骤对于产生正确答案是不必要的。为探究此问题，我们首先进行了一项系统性研究，以考察模型达到稳定决策所需的最少推理量。我们发现，在数学推理等任务上，模型通常在完成约60%的推理步骤后即收敛至最终答案，这表明剩余内容存在大量冗余。基于这些发现，我们提出了三种提升推理效率的推断时策略：（1）通过答案一致性实现早期停止，（2）增强生成推理结束信号的概率，以及（3）一种基于内部激活学习何时停止的监督方法。在五个基准测试和五个开源权重LLM上的实验表明，我们的方法能显著减少令牌使用量，同时几乎不降低或完全不降低准确率。特别是在NaturalQuestions数据集上，答案一致性方法在进一步提升准确率的同时，减少了超过40%的令牌使用量。我们的工作凸显了在推断时运行的高性价比推理方法的重要性，为实际应用提供了切实益处。