title: OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for
    Over-Reasoning Mitigation
summary: Recent advanced large reasoning models (LRMs) leverage extended
  chain-of-thought (CoT) reasoning to solve complex tasks, achieving
  state-of-the-art performance. Despite their success, we identify a critical
  issue: a substantial portion of simple tasks solved by LRMs can also be
  addressed by non-reasoning LLMs using significantly fewer tokens, indicating
  the complex reasoning may not always be necessary. To address this, we
  systematically analyze the reasoning trajectories of LRMs and present a method
  utilizing identified paradigms and LLM-Judge to classify these trajectories as
  either Redundant Reasoning or Essential Reasoning. And we introduce OThink-R1,
  a method that prunes redundant reasoning steps while preserving logical
  validity. OThink-R1 dynamically employs the non-thinking mode (fast-thinking)
  for straightforward problems while engaging in deliberate thinking
  (slow-thinking) for complex problems. Experiments across mathematical and
  question-answering tasks demonstrate that OThink-R1 reduces reasoning
  redundancy by almost 23\% on average without compromising accuracy, offering
  practical guidelines for efficient reasoning models. The code is available at
  https://github.com/AgenticIR-Lab/OThink-R1.
paper_authors._id: 683ff28ae06dd31ada292230
paper_authors.name: Shengjia Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:54:06.870Z
paper_authors._id: 683fec6e84131e802ab1a746
paper_authors.avatarUrl: /avatars/5357ef43d963c6765cd3d3f3d1c4e535.svg
paper_authors.fullname: Shengjia Zhang
paper_authors.type: user
paper_authors.user: Cynthia-1628
paper_authors._id: 683ff28ae06dd31ada292231
paper_authors.name: Junjie Wu
paper_authors._id: 683ff28ae06dd31ada292232
paper_authors.name: Jiawei Chen
paper_authors._id: 683ff28ae06dd31ada292233
paper_authors.name: Changwang Zhang
paper_authors._id: 683ff28ae06dd31ada292234
paper_authors.name: Xingyu Lou
paper_authors._id: 683ff28ae06dd31ada292235
paper_authors.name: Wangchunshu Zhou
paper_authors._id: 683ff28ae06dd31ada292236
paper_authors.name: Sheng Zhou
paper_authors._id: 683ff28ae06dd31ada292237
paper_authors.name: Can Wang
paper_authors._id: 683ff28ae06dd31ada292238
paper_authors.name: Jun Wang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-04T07:15:23.472Z
paper_authors._id: 66da6c9e84f243eba3b49cf1
paper_authors.avatarUrl: /avatars/d30d96025141d68f18a28005a5d6f5af.svg
paper_authors.fullname: junwang.lu
paper_authors.type: user
paper_authors.user: jwanglux
paper_title: OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for
    Over-Reasoning Mitigation
paper_summary: Recent advanced large reasoning models (LRMs) leverage extended
  chain-of-thought (CoT) reasoning to solve complex tasks, achieving
  state-of-the-art performance. Despite their success, we identify a critical
  issue: a substantial portion of simple tasks solved by LRMs can also be
  addressed by non-reasoning LLMs using significantly fewer tokens, indicating
  the complex reasoning may not always be necessary. To address this, we
  systematically analyze the reasoning trajectories of LRMs and present a method
  utilizing identified paradigms and LLM-Judge to classify these trajectories as
  either Redundant Reasoning or Essential Reasoning. And we introduce OThink-R1,
  a method that prunes redundant reasoning steps while preserving logical
  validity. OThink-R1 dynamically employs the non-thinking mode (fast-thinking)
  for straightforward problems while engaging in deliberate thinking
  (slow-thinking) for complex problems. Experiments across mathematical and
  question-answering tasks demonstrate that OThink-R1 reduces reasoning
  redundancy by almost 23\% on average without compromising accuracy, offering
  practical guidelines for efficient reasoning models. The code is available at
  https://github.com/AgenticIR-Lab/OThink-R1.
paper_ai_summary: OThink-R1 is introduced to reduce reasoning redundancy in complex problem-solving by classifying reasoning steps as essential or redundant and dynamically switching thinking modes based on task complexity.
paper_ai_keywords: chain-of-thought (CoT) reasoning large reasoning models (LRMs) non-reasoning LLMs reasoning trajectories LLM-Judge Redundant Reasoning Essential Reasoning OThink-R1 fast-thinking slow-thinking mathematical tasks question-answering tasks