OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for
  Over-Reasoning Mitigation
Authors: name: Shengjia Zhang, name: Junjie Wu, name: Jiawei Chen, name: Changwang Zhang, name: Xingyu Lou, name: Wangchunshu Zhou, name: Sheng Zhou, name: Can Wang, name: Jun Wang

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.