title: Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
summary: Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable
  abilities in complex reasoning tasks by scaling test-time compute and
  exhibiting human-like deep thinking. However, we identify a phenomenon we term
  underthinking, where o1-like LLMs frequently switch between different reasoning
  thoughts without sufficiently exploring promising paths to reach a correct
  solution. This behavior leads to inadequate depth of reasoning and decreased
  performance, particularly on challenging mathematical problems. To
  systematically analyze this issue, we conduct experiments on three challenging
  test sets and two representative open-source o1-like models, revealing that
  frequent thought switching correlates with incorrect responses. We introduce a
  novel metric to quantify underthinking by measuring token efficiency in
  incorrect answers. To address underthinking, we propose a decoding strategy
  with thought switching penalty TIP that discourages premature transitions
  between thoughts, encouraging deeper exploration of each reasoning path.
  Experimental results demonstrate that our approach improves accuracy across
  challenging datasets without requiring model fine-tuning. Our findings
  contribute to understanding reasoning inefficiencies in o1-like LLMs and offer
  a practical solution to enhance their problem-solving capabilities.
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paper_authors.name: Yue Wang
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paper_authors.name: Qiuzhi Liu
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paper_authors.name: Jiahao Xu
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paper_authors.fullname: Jiahao Xu
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paper_authors.name: Tian Liang
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paper_authors.name: Xingyu Chen
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paper_authors.name: Zhiwei He
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paper_authors.fullname: Zhiwei He
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paper_authors.name: Linfeng Song
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paper_authors.fullname: Linfeng Song
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paper_authors.user: freesunshine0316
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paper_authors.name: Dian Yu
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paper_authors.fullname: Dian Yu
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paper_authors.user: yudian
paper_authors._id: 679c5ca666c379e215bc9e7c
paper_authors.name: Juntao Li
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paper_authors.fullname: Juntao Li
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paper_authors._id: 679c5ca666c379e215bc9e7d
paper_authors.name: Zhuosheng Zhang
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paper_authors.fullname: Zhuosheng Zhang
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paper_authors.name: Rui Wang
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paper_authors.name: Zhaopeng Tu
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paper_authors.fullname: Zhaopeng Tu
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paper_authors.name: Haitao Mi
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paper_authors.fullname: Haitao Mi
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paper_authors.user: haitaominlp
paper_authors._id: 679c5ca666c379e215bc9e81
paper_authors.name: Dong Yu
paper_title: Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
paper_summary: Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable
  abilities in complex reasoning tasks by scaling test-time compute and
  exhibiting human-like deep thinking. However, we identify a phenomenon we term
  underthinking, where o1-like LLMs frequently switch between different reasoning
  thoughts without sufficiently exploring promising paths to reach a correct
  solution. This behavior leads to inadequate depth of reasoning and decreased
  performance, particularly on challenging mathematical problems. To
  systematically analyze this issue, we conduct experiments on three challenging
  test sets and two representative open-source o1-like models, revealing that
  frequent thought switching correlates with incorrect responses. We introduce a
  novel metric to quantify underthinking by measuring token efficiency in
  incorrect answers. To address underthinking, we propose a decoding strategy
  with thought switching penalty TIP that discourages premature transitions
  between thoughts, encouraging deeper exploration of each reasoning path.
  Experimental results demonstrate that our approach improves accuracy across
  challenging datasets without requiring model fine-tuning. Our findings
  contribute to understanding reasoning inefficiencies in o1-like LLMs and offer
  a practical solution to enhance their problem-solving capabilities.
paper_ai_keywords: large language models (LLMs) underthinking evaluating thoughts reasoning paths human-like deep thinking complex reasoning tasks scaling test-time compute token efficiency thought switching penalty (TIP)