Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
LARG；Research Center for Social Computing and Interactive Robotics（社会计算与交互机器人研究中心）；Harbin Institute of Technology（哈尔滨工业大学）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；The University of Hong Kong（香港大学）；Fudan University（复旦大学）
Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wanxiang Che
近年来，推理大语言模型（RLLMs）如 OpenAI-O1 和 DeepSeek-R1 取得的进展，展示了它们在数学和编程等复杂领域中令人印象深刻的能力。其成功的一个核心因素在于长链思维（Long CoT）特性的应用，这些特性增强了推理能力，使模型能够解决复杂问题。然而，尽管有这些发展，目前仍缺乏对 Long CoT 的全面综述，这限制了我们对其与传统短链思维（Short CoT）区别的理解，也使关于“过度思考”和“推理时扩展”等问题的持续争论变得更加复杂。本综述旨在通过提供关于 Long CoT 的统一视角来填补这一空白。（1）我们首先将 Long CoT 与 Short CoT 区分开来，并提出一种新的分类法来对当前的推理范式进行分类。（2）接着，我们探讨 Long CoT 的关键特性：深度推理、广泛探索和可行反思，这些特性使模型能够处理更复杂的任务，并产生比浅层 Short CoT 更高效、更连贯的结果。（3）然后，我们研究具有这些特性的 Long CoT 出现的关键现象，包括过度思考和推理时扩展，为这些过程在实践中如何表现提供见解。（4）最后，我们识别出重要的研究空白，并指出有前景的未来方向，包括多模态推理的整合、效率提升和增强的知识框架。通过提供结构化的概述，本综述旨在启发未来的研究，并推动人工智能中逻辑推理的发展。
英文摘要：
Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "inference-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and inference-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence.
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. Th…
Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to i…
ThoughtSource: A central hub for large language model reasoning data
Medical University of Vienna（维也纳医科大学）；Technical University of Denmark（丹麦技术大学）；Copenhagen University Hospital（哥本哈根大学医院）；The Royal Institute of Technology (KTH)（皇家理工学院（KTH））
Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）；Didichuxing Co. Ltd（滴滴出行有限公司）；CSE, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering
College of Zhiyuan, Shanghai Jiao Tong University（上海交通大学致远学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；Shanghai Jiao Tong University（上海交通大学）
Distilling Mathematical Reasoning Capabilities into Small Language Models
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；State University of New York at Buffalo（纽约州立大学布法罗分校）；Huawei International, Singapore（新加坡华为国际）
