title: A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
summary: Enabling Large Language Models (LLMs) to handle a wider range of complex
  tasks (e.g., coding, math) has drawn great attention from many researchers. As
  LLMs continue to evolve, merely increasing the number of model parameters
  yields diminishing performance improvements and heavy computational costs.
  Recently, OpenAI's o1 model has shown that inference strategies (i.e.,
  Test-time Compute methods) can also significantly enhance the reasoning
  capabilities of LLMs. However, the mechanisms behind these methods are still
  unexplored. In our work, to investigate the reasoning patterns of o1, we
  compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent
  Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general
  reasoning benchmarks in three domains (i.e., math, coding, commonsense
  reasoning). Specifically, first, our experiments show that the o1 model has
  achieved the best performance on most datasets. Second, as for the methods of
  searching diverse responses (e.g., BoN), we find the reward models' capability
  and the search space both limit the upper boundary of these methods. Third, as
  for the methods that break the problem into many sub-problems, the Agent
  Workflow has achieved better performance than Step-wise BoN due to the
  domain-specific system prompt for planning better reasoning processes. Fourth,
  it is worth mentioning that we have summarized six reasoning patterns of o1,
  and provided a detailed analysis on several reasoning benchmarks.
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paper_authors.name: Siwei Wu
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paper_authors.name: Minghao Liu
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paper_authors.fullname: Jialong Wu
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paper_authors.name: Jiachen Ma
paper_authors._id: 6712f4b0b04bffe3d19b6698
paper_authors.name: Yizhi Li
paper_authors._id: 6712f4b0b04bffe3d19b6699
paper_authors.name: Jian Yang
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paper_authors.name: Wangchunshu Zhou
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paper_authors.name: Junbo Zhao
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paper_authors.name: Zhaoxiang Zhang
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paper_authors.name: Wenhao Huang
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paper_authors.name: Ge Zhang
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paper_authors.fullname: Ge Zhang
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paper_authors.name: Chenghua Lin
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paper_authors.name: J. H. Liu
paper_title: A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
paper_summary: Enabling Large Language Models (LLMs) to handle a wider range of complex
  tasks (e.g., coding, math) has drawn great attention from many researchers. As
  LLMs continue to evolve, merely increasing the number of model parameters
  yields diminishing performance improvements and heavy computational costs.
  Recently, OpenAI's o1 model has shown that inference strategies (i.e.,
  Test-time Compute methods) can also significantly enhance the reasoning
  capabilities of LLMs. However, the mechanisms behind these methods are still
  unexplored. In our work, to investigate the reasoning patterns of o1, we
  compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent
  Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general
  reasoning benchmarks in three domains (i.e., math, coding, commonsense
  reasoning). Specifically, first, our experiments show that the o1 model has
  achieved the best performance on most datasets. Second, as for the methods of
  searching diverse responses (e.g., BoN), we find the reward models' capability
  and the search space both limit the upper boundary of these methods. Third, as
  for the methods that break the problem into many sub-problems, the Agent
  Workflow has achieved better performance than Step-wise BoN due to the
  domain-specific system prompt for planning better reasoning processes. Fourth,
  it is worth mentioning that we have summarized six reasoning patterns of o1,
  and provided a detailed analysis on several reasoning benchmarks.
paper_ai_summary: OpenAI's o1 model enhances LLM reasoning capabilities through Test-time Compute methods, outperforming existing strategies across math, coding, and commonsense reasoning tasks.
paper_ai_keywords: LLMs Large Language Models Test-time Compute o1 model BoN Step-wise BoN Agent Workflow Self-Refine GPT-4o reasoning patterns