从自我怀疑视角重新审视长思维链中的过度思考
Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt
AI总结：
针对推理大模型长思维链中的过度思考问题，该研究从自我怀疑视角进行定量分析，并提出一种提示方法，通过先质疑输入问题有效性再简明作答，有效减少了推理步骤和自我怀疑，在多个模型与数据集上取得显著提升。
摘要：
推理大语言模型（RLLMs）在复杂任务上展现了出色的性能，这很大程度上归功于采用了长思维链（Long CoT）推理。然而，它们经常表现出过度思考——即使在得出正确答案后，仍执行不必要的推理步骤。先前的工作主要通过基于样本的长CoT观察，对过度思考进行定性分析。相比之下，我们从自我怀疑的视角对过度思考进行了定量分析，其特征是耗费过多的token去重新验证已经正确的答案。我们发现，自我怀疑显著导致了过度思考。作为应对，我们引入了一种简单有效的提示方法，以减少模型对输入问题的过度依赖，从而避免自我怀疑。具体而言，我们首先提示模型质疑输入问题的有效性，然后基于该评估结果进行简明回答。在三个数学推理任务和四个包含缺失前提的数据集上的实验表明，我们的方法大幅减少了答案长度，并在4个广泛使用的RLLMs上，几乎所有数据集上都取得了显著提升。进一步分析表明，我们的方法有效减少了推理步骤的数量并降低了自我怀疑。
英文摘要：
Reasoning Large Language Models (RLLMs) have demonstrated impressive performance on complex tasks, largely due to the adoption of Long Chain-of-Thought (Long CoT) reasoning. However, they often exhibit overthinking -- performing unnecessary reasoning steps even after arriving at the correct answer. Prior work has largely focused on qualitative analyses of overthinking through sample-based observations of long CoTs. In contrast, we present a quantitative analysis of overthinking from the perspective of self-doubt, characterized by excessive token usage devoted to re-verifying already-correct answer. We find that self-doubt significantly contributes to overthinking. In response, we introduce a simple and effective prompting method to reduce the model's over-reliance on input questions, thereby avoiding self-doubt. Specifically, we first prompt the model to question the validity of the input question, and then respond concisely based on the outcome of that evaluation. Experiments on three mathematical reasoning tasks and four datasets with missing premises demonstrate that our method substantially reduces answer length and yields significant improvements across nearly all datasets upon 4 widely-used RLLMs. Further analysis demonstrates that our method effectively minimizes the number of reasoning steps and reduces self-doubt.
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