OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for
  Over-Reasoning Mitigation
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.
683ff28ae06dd31ada292230
Shengjia Zhang
claimed_verified
2025-06-04T08:54:06.870Z
683fec6e84131e802ab1a746
/avatars/5357ef43d963c6765cd3d3f3d1c4e535.svg
Shengjia Zhang
user
Cynthia-1628
683ff28ae06dd31ada292231
Junjie Wu
683ff28ae06dd31ada292232
Jiawei Chen
683ff28ae06dd31ada292233
Changwang Zhang
683ff28ae06dd31ada292234
Xingyu Lou
683ff28ae06dd31ada292235
Wangchunshu Zhou
claimed_verified
2025-06-18T12:17:28.730Z
628c8598ef14f971b698107f
/avatars/3a4ad87e6b5f9e836a1160d869df1447.svg
Zhou
user
Wangchunshu
683ff28ae06dd31ada292236
Sheng Zhou
683ff28ae06dd31ada292237
Can Wang
683ff28ae06dd31ada292238
Jun Wang
extracted_pending
2025-06-04T07:15:23.472Z
66da6c9e84f243eba3b49cf1
/avatars/d30d96025141d68f18a28005a5d6f5af.svg
junwang.lu
user
jwanglux
OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for
  Over-Reasoning Mitigation
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.
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.
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