Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

By Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wangxiang Che. arXiv.org, 2025

330 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/0d320beb4a5304a8bd03bb83eba1a8196c601be1

Citations in the corpus

• Video-R1: Reinforcing Video Reasoning in MLLMs (2025, unknown): “For example, Open Reasoner Zero [7] and Kimi k1.”
• WebThinker: Empowering Large Reasoning Models with Deep Research Capability (2025, unknown): “A solution comprises a logical reasoning chain R and a final output y (which could be an answer or a signal indicating completion).”
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (2025, unknown): “…LLMs, also referred to as reasoning-capable models or Large Reasoning Models (LRMs) [98] such as OpenAI o1 [65] and DeepSeek-R1 [33], has significantly improved performance in System-2 reasoning domains [47], particularly in challenging mathematics [18,37] and programming tasks [7,19].”