title: Can Large Language Models Detect Errors in Long Chain-of-Thought
    Reasoning?
summary: Recently, o1-like models have drawn significant attention, where these models
  produce the long Chain-of-Thought (CoT) reasoning steps to improve the
  reasoning abilities of existing Large Language Models (LLMs). In this paper, to
  understand the qualities of these long CoTs and measure the critique abilities
  of existing LLMs on these long CoTs, we introduce the DeltaBench, including the
  generated long CoTs from different o1-like models (e.g., QwQ, DeepSeek-R1) for
  different reasoning tasks (e.g., Math, Code, General Reasoning), to measure the
  ability to detect errors in long CoT reasoning. Based on DeltaBench, we first
  perform fine-grained analysis of the generated long CoTs to discover the
  effectiveness and efficiency of different o1-like models. Then, we conduct
  extensive evaluations of existing process reward models (PRMs) and critic
  models to detect the errors of each annotated process, which aims to
  investigate the boundaries and limitations of existing PRMs and critic models.
  Finally, we hope that DeltaBench could guide developers to better understand
  the long CoT reasoning abilities of their models.
paper_authors._id: 67bfe435ca6e3c22b6e29442
paper_authors.name: Yancheng He
paper_authors._id: 67bfe435ca6e3c22b6e29443
paper_authors.name: Shilong Li
paper_authors._id: 67bfe435ca6e3c22b6e29444
paper_authors.name: Jiaheng Liu
paper_authors._id: 67bfe435ca6e3c22b6e29445
paper_authors.name: Weixun Wang
paper_authors._id: 67bfe435ca6e3c22b6e29446
paper_authors.name: Xingyuan Bu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-09T14:37:05.802Z
paper_authors._id: 6444e7765691ca69b0d95856
paper_authors.avatarUrl: /avatars/4ae5001e7c7dea89c4deaf2b05436857.svg
paper_authors.fullname: Xingyuan Bu
paper_authors.type: user
paper_authors.user: sefira32
paper_authors._id: 67bfe435ca6e3c22b6e29447
paper_authors.name: Ge Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T09:13:58.959Z
paper_authors._id: 638efcf4c67af472d316d424
paper_authors.avatarUrl: /avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
paper_authors.fullname: Ge Zhang
paper_authors.type: user
paper_authors.user: zhangysk
paper_authors._id: 67bfe435ca6e3c22b6e29448
paper_authors.name: Zhongyuan Peng
paper_authors._id: 67bfe435ca6e3c22b6e29449
paper_authors.name: Zhaoxiang Zhang
paper_authors._id: 67bfe435ca6e3c22b6e2944a
paper_authors.name: Wenbo Su
paper_authors._id: 67bfe435ca6e3c22b6e2944b
paper_authors.name: Bo Zheng
paper_title: Can Large Language Models Detect Errors in Long Chain-of-Thought
    Reasoning?
paper_summary: Recently, o1-like models have drawn significant attention, where these models
  produce the long Chain-of-Thought (CoT) reasoning steps to improve the
  reasoning abilities of existing Large Language Models (LLMs). In this paper, to
  understand the qualities of these long CoTs and measure the critique abilities
  of existing LLMs on these long CoTs, we introduce the DeltaBench, including the
  generated long CoTs from different o1-like models (e.g., QwQ, DeepSeek-R1) for
  different reasoning tasks (e.g., Math, Code, General Reasoning), to measure the
  ability to detect errors in long CoT reasoning. Based on DeltaBench, we first
  perform fine-grained analysis of the generated long CoTs to discover the
  effectiveness and efficiency of different o1-like models. Then, we conduct
  extensive evaluations of existing process reward models (PRMs) and critic
  models to detect the errors of each annotated process, which aims to
  investigate the boundaries and limitations of existing PRMs and critic models.
  Finally, we hope that DeltaBench could guide developers to better understand
  the long CoT reasoning abilities of their models.
paper_ai_summary: DeltaBench evaluates the ability of models and process reward models to detect errors in long Chain-of-Thought reasoning generated by o1-like models across various reasoning tasks.
paper_ai_keywords: Chain-of-Thought Large Language Models DeltaBench o1-like models QwQ DeepSeek-R1 Math Code General Reasoning process reward models critic models