title: Stop Overthinking: A Survey on Efficient Reasoning for Large Language
    Models
summary: Large Language Models (LLMs) have demonstrated remarkable capabilities in
  complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as
  OpenAI o1 and DeepSeek-R1, have further improved performance in System-2
  reasoning domains like mathematics and programming by harnessing supervised
  fine-tuning (SFT) and reinforcement learning (RL) techniques to enhance the
  Chain-of-Thought (CoT) reasoning. However, while longer CoT reasoning sequences
  improve performance, they also introduce significant computational overhead due
  to verbose and redundant outputs, known as the "overthinking phenomenon". In
  this paper, we provide the first structured survey to systematically
  investigate and explore the current progress toward achieving efficient
  reasoning in LLMs. Overall, relying on the inherent mechanism of LLMs, we
  categorize existing works into several key directions: (1) model-based
  efficient reasoning, which considers optimizing full-length reasoning models
  into more concise reasoning models or directly training efficient reasoning
  models; (2) reasoning output-based efficient reasoning, which aims to
  dynamically reduce reasoning steps and length during inference; (3) input
  prompts-based efficient reasoning, which seeks to enhance reasoning efficiency
  based on input prompt properties such as difficulty or length control.
  Additionally, we introduce the use of efficient data for training reasoning
  models, explore the reasoning capabilities of small language models, and
  discuss evaluation methods and benchmarking.
paper_authors._id: 67dcdbfc71027d42fa46e3f2
paper_authors.name: Yang Sui
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paper_authors.name: Yu-Neng Chuang
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paper_authors.name: Guanchu Wang
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paper_authors.fullname: Guanchu
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paper_authors.name: Jiamu Zhang
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paper_authors.fullname: Jiamu 'Morris' Zhang
paper_authors.type: user
paper_authors.user: morriszjm
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paper_authors.name: Tianyi Zhang
paper_authors._id: 67dcdbfc71027d42fa46e3f7
paper_authors.name: Jiayi Yuan
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paper_authors.fullname: Jiayi Yuan
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paper_authors.name: Hongyi Liu
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paper_authors._id: 65c540e2735dd9c81625fadd
paper_authors.avatarUrl: /avatars/36713db387abb001cc1e20644ff1f1d4.svg
paper_authors.fullname: Hongyi Liu
paper_authors.type: user
paper_authors.user: HongyiLiuAI
paper_authors._id: 67dcdbfc71027d42fa46e3f9
paper_authors.name: Andrew Wen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/57a3a9c5175d0b23cc7d6cab0fec08b7.svg
paper_authors.fullname: andrew wen
paper_authors.type: user
paper_authors.user: andrewwen
paper_authors._id: 67dcdbfc71027d42fa46e3fa
paper_authors.name: Shaochen
paper_authors._id: 67dcdbfc71027d42fa46e3fb
paper_authors.name: Zhong
paper_authors._id: 67dcdbfc71027d42fa46e3fc
paper_authors.name: Hanjie Chen
paper_authors._id: 67dcdbfc71027d42fa46e3fd
paper_authors.name: Xia Hu
paper_title: Stop Overthinking: A Survey on Efficient Reasoning for Large Language
    Models
paper_summary: Large Language Models (LLMs) have demonstrated remarkable capabilities in
  complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as
  OpenAI o1 and DeepSeek-R1, have further improved performance in System-2
  reasoning domains like mathematics and programming by harnessing supervised
  fine-tuning (SFT) and reinforcement learning (RL) techniques to enhance the
  Chain-of-Thought (CoT) reasoning. However, while longer CoT reasoning sequences
  improve performance, they also introduce significant computational overhead due
  to verbose and redundant outputs, known as the "overthinking phenomenon". In
  this paper, we provide the first structured survey to systematically
  investigate and explore the current progress toward achieving efficient
  reasoning in LLMs. Overall, relying on the inherent mechanism of LLMs, we
  categorize existing works into several key directions: (1) model-based
  efficient reasoning, which considers optimizing full-length reasoning models
  into more concise reasoning models or directly training efficient reasoning
  models; (2) reasoning output-based efficient reasoning, which aims to
  dynamically reduce reasoning steps and length during inference; (3) input
  prompts-based efficient reasoning, which seeks to enhance reasoning efficiency
  based on input prompt properties such as difficulty or length control.
  Additionally, we introduce the use of efficient data for training reasoning
  models, explore the reasoning capabilities of small language models, and
  discuss evaluation methods and benchmarking.
paper_ai_summary: This survey examines progress and methods for improving efficient reasoning in Large Language Models (LLMs), focusing on model, output, and input prompt optimizations, as well as the use of efficient data and small models.
paper_ai_keywords: Large Language Models (LLMs) Large Reasoning Models (LRMs) supervised fine-tuning (SFT) reinforcement learning (RL) Chain-of-Thought (CoT) reasoning overthinking phenomenon efficient reasoning model-based efficient reasoning reasoning output-based efficient reasoning input prompts-based efficient reasoning efficient data small language models evaluation methods benchmarking