无需过度思考：高效R1风格大型推理模型综述
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
August 4, 2025
作者: Linan Yue, Yichao Du, Yizhi Wang, Weibo Gao, Fangzhou Yao, Li Wang, Ye Liu, Ziyu Xu, Qi Liu, Shimin Di, Min-Ling Zhang
cs.AI
摘要
近年来，大型推理模型（LRMs）因其在处理复杂任务中的卓越表现逐渐成为研究热点。其中，DeepSeek R1凭借其出色的性能和开源特性，引起了广泛关注，推动了R1风格LRMs的研究进展。与传统的大型语言模型（LLMs）不同，这些模型通过引入长链思维和强化学习中的自我反思等机制，在推理过程中增强了逻辑推理和决策能力。然而，随着这些模型的广泛应用，过度思考的问题逐渐显现。具体而言，在生成答案时，这些模型往往构建过长的推理链，包含冗余或重复的步骤，这不仅降低了推理效率，还可能影响最终答案的准确性。为此，多种高效推理方法被提出，旨在不损害模型性能和推理能力的前提下，缩短推理路径的长度。通过系统梳理当前高效推理方法领域的研究进展，我们基于单模型优化与模型协作的视角，将现有工作分为两大方向：（1）单模型高效推理，专注于提升单个模型的推理效率；（2）模型协作高效推理，探索通过多模型协作优化推理路径。此外，我们维护了一个公开的GitHub仓库，持续追踪高效推理方法的最新进展。
English
Recently, Large Reasoning Models (LRMs) have gradually become a research
hotspot due to their outstanding performance in handling complex tasks. Among
them, DeepSeek R1 has garnered significant attention for its exceptional
performance and open-source nature, driving advancements in the research of
R1-style LRMs. Unlike traditional Large Language Models (LLMs), these models
enhance logical deduction and decision-making capabilities during reasoning by
incorporating mechanisms such as long chain-of-thought and self-reflection
through reinforcement learning. However, with the widespread application of
these models, the problem of overthinking has gradually emerged. Specifically,
when generating answers, these models often construct excessively long
reasoning chains with redundant or repetitive steps, which leads to reduced
reasoning efficiency and may affect the accuracy of the final answer. To this
end, various efficient reasoning methods have been proposed, aiming to reduce
the length of reasoning paths without compromising model performance and
reasoning capability. By reviewing the current research advancements in the
field of efficient reasoning methods systematically, we categorize existing
works into two main directions based on the lens of single-model optimization
versus model collaboration: (1) Efficient Reasoning with Single Model, which
focuses on improving the reasoning efficiency of individual models; and (2)
Efficient Reasoning with Model Collaboration, which explores optimizing
reasoning paths through collaboration among multiple models. Besides, we
maintain a public GitHub repository that tracks the latest progress in
efficient reasoning methods.