Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for
  Large Language Models
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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Rui Wang
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2025-04-02T09:52:11.104Z
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WANG Rui
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Ray121381
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Hongru Wang
admin_assigned
2025-04-02T09:52:28.243Z
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Hongru Wang
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Merlin-Hongru
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Boyang Xue
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2025-04-02T09:52:39.163Z
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XUE Boyang
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BeyondHsueh
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Jianhui Pang
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2025-04-02T09:52:45.274Z
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Jianhuipang
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pangjh3
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Shudong Liu
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2025-04-02T09:52:51.029Z
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Shudong Liu
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Sudanl
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Yi Chen
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2025-04-02T09:52:58.486Z
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Yichen
user
Yichen
67eca439a62c82ed64354e3c
Jiahao Qiu
claimed_verified
2025-05-28T19:15:01.345Z
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Jiahao Qiu
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jiahaoq
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Derek Fai Wong
admin_assigned
2025-04-02T09:53:28.254Z
648bd523805e4bcc541ec320
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Derek Wong
user
derekfw
67eca439a62c82ed64354e3e
Heng Ji
67eca439a62c82ed64354e3f
Kam-Fai Wong
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for
  Large Language Models
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.
A survey on reasoning economy in LLMs explores causes of inefficiency, analyzes different reasoning patterns, and offers solutions to balance performance and computational costs.
Large Language Models reasoning economy System 1 System 2 reasoning inefficiency test-time inference post-training reasoning patterns