title: Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for
    Large Language Models
summary: Recent advancements in Large Language Models (LLMs) have significantly
  enhanced their ability to perform complex reasoning tasks, transitioning from
  fast and intuitive thinking (System 1) to slow and deep reasoning (System 2).
  While System 2 reasoning improves task accuracy, it often incurs substantial
  computational costs due to its slow thinking nature and inefficient or
  unnecessary reasoning behaviors. In contrast, System 1 reasoning is
  computationally efficient but leads to suboptimal performance. Consequently, it
  is critical to balance the trade-off between performance (benefits) and
  computational costs (budgets), giving rise to the concept of reasoning economy.
  In this survey, we provide a comprehensive analysis of reasoning economy in
  both the post-training and test-time inference stages of LLMs, encompassing i)
  the cause of reasoning inefficiency, ii) behavior analysis of different
  reasoning patterns, and iii) potential solutions to achieve reasoning economy.
  By offering actionable insights and highlighting open challenges, we aim to
  shed light on strategies for improving the reasoning economy of LLMs, thereby
  serving as a valuable resource for advancing research in this evolving area. We
  also provide a public repository to continually track developments in this
  fast-evolving field.
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paper_authors.name: Rui Wang
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paper_authors.name: Boyang Xue
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paper_authors.fullname: XUE Boyang
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paper_authors.name: Jianhui Pang
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paper_authors.name: Shudong Liu
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paper_authors.fullname: Shudong Liu
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paper_authors.name: Yi Chen
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paper_authors.fullname: Yichen
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paper_authors.name: Jiahao Qiu
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paper_authors.fullname: Jiahao Qiu
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paper_authors.user: jiahaoq
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paper_authors.name: Derek Fai Wong
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paper_authors.fullname: Derek Wong
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paper_authors.user: derekfw
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paper_authors.name: Heng Ji
paper_authors._id: 67eca439a62c82ed64354e3f
paper_authors.name: Kam-Fai Wong
paper_title: Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for
    Large Language Models
paper_summary: Recent advancements in Large Language Models (LLMs) have significantly
  enhanced their ability to perform complex reasoning tasks, transitioning from
  fast and intuitive thinking (System 1) to slow and deep reasoning (System 2).
  While System 2 reasoning improves task accuracy, it often incurs substantial
  computational costs due to its slow thinking nature and inefficient or
  unnecessary reasoning behaviors. In contrast, System 1 reasoning is
  computationally efficient but leads to suboptimal performance. Consequently, it
  is critical to balance the trade-off between performance (benefits) and
  computational costs (budgets), giving rise to the concept of reasoning economy.
  In this survey, we provide a comprehensive analysis of reasoning economy in
  both the post-training and test-time inference stages of LLMs, encompassing i)
  the cause of reasoning inefficiency, ii) behavior analysis of different
  reasoning patterns, and iii) potential solutions to achieve reasoning economy.
  By offering actionable insights and highlighting open challenges, we aim to
  shed light on strategies for improving the reasoning economy of LLMs, thereby
  serving as a valuable resource for advancing research in this evolving area. We
  also provide a public repository to continually track developments in this
  fast-evolving field.
paper_ai_summary: A survey on reasoning economy in LLMs explores causes of inefficiency, analyzes different reasoning patterns, and offers solutions to balance performance and computational costs.
paper_ai_keywords: Large Language Models reasoning economy System 1 System 2 reasoning inefficiency test-time inference post-training reasoning patterns