title: PATS: Process-Level Adaptive Thinking Mode Switching
summary: Current large-language models (LLMs) typically adopt a fixed reasoning
  strategy, either simple or complex, for all questions, regardless of their
  difficulty. This neglect of variation in task and reasoning process complexity
  leads to an imbalance between performance and efficiency. Existing methods
  attempt to implement training-free fast-slow thinking system switching to
  handle problems of varying difficulty, but are limited by coarse-grained
  solution-level strategy adjustments. To address this issue, we propose a novel
  reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS),
  which enables LLMs to dynamically adjust their reasoning strategy based on the
  difficulty of each step, optimizing the balance between accuracy and
  computational efficiency. Our approach integrates Process Reward Models (PRMs)
  with Beam Search, incorporating progressive mode switching and bad-step penalty
  mechanisms. Experiments on diverse mathematical benchmarks demonstrate that our
  methodology achieves high accuracy while maintaining moderate token usage. This
  study emphasizes the significance of process-level, difficulty-aware reasoning
  strategy adaptation, offering valuable insights into efficient inference for
  LLMs.
paper_authors._id: 68355ce06a9c239ada09f97b
paper_authors.name: Yi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:49:06.169Z
paper_authors._id: 66e0404662d6ab4f1107580f
paper_authors.avatarUrl: /avatars/ef71694fea5482078a637a3869e30d19.svg
paper_authors.fullname: Yi Wang
paper_authors.type: user
paper_authors.user: Yi53
paper_authors._id: 68355ce06a9c239ada09f97c
paper_authors.name: Junxiao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:01.261Z
paper_authors._id: 68356f5db243fb809813a715
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/68356f5db243fb809813a715/grhHvANfDRp75rMJxWlQo.jpeg
paper_authors.fullname: LiuJunxiao
paper_authors.type: user
paper_authors.user: master-lan
paper_authors._id: 68355ce06a9c239ada09f97d
paper_authors.name: Shimao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:34:46.475Z
paper_authors._id: 65080dc63fc966d1bbba485d
paper_authors.avatarUrl: /avatars/347890233f2316e7f7a04d652b2378bb.svg
paper_authors.fullname: Shimao Zhang
paper_authors.type: user
paper_authors.user: Shimao-Zhang
paper_authors._id: 68355ce06a9c239ada09f97e
paper_authors.name: Jiajun Chen
paper_authors._id: 68355ce06a9c239ada09f97f
paper_authors.name: Shujian Huang
paper_title: PATS: Process-Level Adaptive Thinking Mode Switching
paper_summary: Current large-language models (LLMs) typically adopt a fixed reasoning
  strategy, either simple or complex, for all questions, regardless of their
  difficulty. This neglect of variation in task and reasoning process complexity
  leads to an imbalance between performance and efficiency. Existing methods
  attempt to implement training-free fast-slow thinking system switching to
  handle problems of varying difficulty, but are limited by coarse-grained
  solution-level strategy adjustments. To address this issue, we propose a novel
  reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS),
  which enables LLMs to dynamically adjust their reasoning strategy based on the
  difficulty of each step, optimizing the balance between accuracy and
  computational efficiency. Our approach integrates Process Reward Models (PRMs)
  with Beam Search, incorporating progressive mode switching and bad-step penalty
  mechanisms. Experiments on diverse mathematical benchmarks demonstrate that our
  methodology achieves high accuracy while maintaining moderate token usage. This
  study emphasizes the significance of process-level, difficulty-aware reasoning
  strategy adaptation, offering valuable insights into efficient inference for
  LLMs.
paper_ai_summary: PATS enhances LLM efficiency by dynamically adjusting reasoning strategies based on task difficulty, leveraging PRMs and Beam Search.
paper_ai_keywords: large-language models (LLMs) reasoning strategy task and reasoning process complexity training-free fast-slow thinking system switching Process-Level Adaptive Thinking Mode Switching (PATS) Process Reward Models (PRMs) Beam Search progressive mode switching bad-step penalty mechanisms mathematical benchmarks process-level difficulty-aware reasoning strategy adaptation