Can Large Language Models Detect Errors in Long Chain-of-Thought
  Reasoning?
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.
67bfe435ca6e3c22b6e29442
Yancheng He
67bfe435ca6e3c22b6e29443
Shilong Li
67bfe435ca6e3c22b6e29444
Jiaheng Liu
67bfe435ca6e3c22b6e29445
Weixun Wang
67bfe435ca6e3c22b6e29446
Xingyuan Bu
claimed_verified
2025-04-09T14:37:05.802Z
6444e7765691ca69b0d95856
/avatars/4ae5001e7c7dea89c4deaf2b05436857.svg
Xingyuan Bu
user
sefira32
67bfe435ca6e3c22b6e29447
Ge Zhang
claimed_verified
2025-02-27T09:13:58.959Z
638efcf4c67af472d316d424
/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
Ge Zhang
user
zhangysk
67bfe435ca6e3c22b6e29448
Zhongyuan Peng
claimed_verified
2025-07-09T15:11:33.826Z
63299f93688ad82b783aaf20
/avatars/7c11e60e551ef1c62aa2862529e357f5.svg
zhongyuan peng
user
happzy2633
67bfe435ca6e3c22b6e29449
Zhaoxiang Zhang
67bfe435ca6e3c22b6e2944a
Wenbo Su
67bfe435ca6e3c22b6e2944b
Bo Zheng
Can Large Language Models Detect Errors in Long Chain-of-Thought
  Reasoning?
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.
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.
Chain-of-Thought Large Language Models DeltaBench o1-like models QwQ DeepSeek-R1 Math Code General Reasoning process reward models critic models