不要过度思考。为改进大语言模型推理而偏好更短的思考链
Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
- FAIR Team, Meta（Meta FAIR团队）
- The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出short-m@k方法，通过并行生成较短的思考链提高大语言模型推理效率，实验表明其在低计算环境下表现优于传统多数投票。
AI中文摘要：
大语言模型（LLM）的推理严重依赖于在测试时扩展计算量以执行复杂的推理任务，通过生成大量的
英文摘要：
Reasoning large language models (LLMs) heavily rely on scaling test-time compute to perform complex reasoning tasks by generating extensive "thinking" chains. While demonstrating impressive results, this approach incurs significant computational costs and inference time. In this work, we challenge the assumption that long thinking chains results in better reasoning capabilities. We first demonstrate that shorter reasoning chains within individual questions are significantly more likely to yield correct answers - up to 34.5% more accurate than the longest chain sampled for the same question. Based on these results, we suggest short-m@k, a novel reasoning LLM inference method. Our method executes k independent generations in parallel and halts computation once the first m thinking processes are done. The final answer is chosen using majority voting among these m chains. Basic short-1@k demonstrates similar or even superior performance over standard majority voting in low-compute settings - using up to 40% fewer thinking tokens. short-3@k, while slightly less efficient than short-1@k, consistently surpasses majority voting across all compute budgets, while still being substantially faster (up to 33% wall time reduction). To further validate our findings, we finetune LLMs using short, long, and randomly selected reasoning chains. We then observe that training on the shorter ones leads to better performance. Our findings suggest rethinking current methods of test-time compute in reasoning LLMs, emphasizing that longer "thinking" does not necessarily translate to improved performance and can, counter-intuitively, lead to degraded results.
