title: Efficient Reasoning Models: A Survey
summary: Reasoning models have demonstrated remarkable progress in solving complex and
  logic-intensive tasks by generating extended Chain-of-Thoughts (CoTs) prior to
  arriving at a final answer. Yet, the emergence of this "slow-thinking"
  paradigm, with numerous tokens generated in sequence, inevitably introduces
  substantial computational overhead. To this end, it highlights an urgent need
  for effective acceleration. This survey aims to provide a comprehensive
  overview of recent advances in efficient reasoning. It categorizes existing
  works into three key directions: (1) shorter - compressing lengthy CoTs into
  concise yet effective reasoning chains; (2) smaller - developing compact
  language models with strong reasoning capabilities through techniques such as
  knowledge distillation, other model compression techniques, and reinforcement
  learning; and (3) faster - designing efficient decoding strategies to
  accelerate inference. A curated collection of papers discussed in this survey
  is available in our GitHub repository.
paper_authors._id: 67ff8a2b81740ab021196e98
paper_authors.name: Sicheng Feng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T11:57:54.189Z
paper_authors._id: 67a4a26d5e65aa63c6d30e68
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67a4a26d5e65aa63c6d30e68/GtodlJGw-_IL2DTXQTucz.jpeg
paper_authors.fullname: Sicheng Feng
paper_authors.type: user
paper_authors.user: FSCCS
paper_authors._id: 67ff8a2b81740ab021196e99
paper_authors.name: Gongfan Fang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T13:42:05.338Z
paper_authors._id: 646a1939c37ca1e12308fe81
paper_authors.avatarUrl: /avatars/752e9d86018e7d33ad8bcd741203fd86.svg
paper_authors.fullname: Gongfan Fang
paper_authors.type: user
paper_authors.user: Vinnnf
paper_authors._id: 67ff8a2b81740ab021196e9a
paper_authors.name: Xinyin Ma
paper_authors._id: 67ff8a2b81740ab021196e9b
paper_authors.name: Xinchao Wang
paper_title: Efficient Reasoning Models: A Survey
paper_summary: Reasoning models have demonstrated remarkable progress in solving complex and
  logic-intensive tasks by generating extended Chain-of-Thoughts (CoTs) prior to
  arriving at a final answer. Yet, the emergence of this "slow-thinking"
  paradigm, with numerous tokens generated in sequence, inevitably introduces
  substantial computational overhead. To this end, it highlights an urgent need
  for effective acceleration. This survey aims to provide a comprehensive
  overview of recent advances in efficient reasoning. It categorizes existing
  works into three key directions: (1) shorter - compressing lengthy CoTs into
  concise yet effective reasoning chains; (2) smaller - developing compact
  language models with strong reasoning capabilities through techniques such as
  knowledge distillation, other model compression techniques, and reinforcement
  learning; and (3) faster - designing efficient decoding strategies to
  accelerate inference. A curated collection of papers discussed in this survey
  is available in our GitHub repository.
paper_ai_summary: The survey discusses methods to accelerate reasoning models by compressing Chain-of-Thoughts, developing compact models, and designing efficient decoding strategies.
paper_ai_keywords: reasoning models Chain-of-Thoughts (CoTs) knowledge distillation model compression techniques reinforcement learning efficient decoding strategies