Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
The remarkable performance of models like the OpenAI o1 can be attributed to
their ability to emulate human-like long-time thinking during inference. These
models employ extended chain-of-thought (CoT) processes, exploring multiple
strategies to enhance problem-solving capabilities. However, a critical
question remains: How to intelligently and efficiently scale computational
resources during testing. This paper presents the first comprehensive study on
the prevalent issue of overthinking in these models, where excessive
computational resources are allocated for simple problems with minimal benefit.
We introduce novel efficiency metrics from both outcome and process
perspectives to evaluate the rational use of computational resources by o1-like
models. Using a self-training paradigm, we propose strategies to mitigate
overthinking, streamlining reasoning processes without compromising accuracy.
Experimental results show that our approach successfully reduces computational
overhead while preserving model performance across a range of testsets with
varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME.
6773f75a23a7829936cb36bd
Xingyu Chen
6773f75a23a7829936cb36be
Jiahao Xu
6773f75a23a7829936cb36bf
Tian Liang
6773f75a23a7829936cb36c0
Zhiwei He
claimed_verified
2025-01-01T20:12:33.739Z
638439ca834d3558a398d035
https://cdn-avatars.huggingface.co/v1/production/uploads/1669609868550-noauth.png
Zhiwei He
user
zwhe99
6773f75a23a7829936cb36c1
Jianhui Pang
6773f75a23a7829936cb36c2
Dian Yu
claimed_verified
2025-03-12T20:46:22.967Z
62d58fd53bf5e059f7cc3245
/avatars/7a4f3ee4a37245f67efd26749d66a706.svg
Dian Yu
user
yudian
6773f75a23a7829936cb36c3
Linfeng Song
claimed_verified
2025-07-11T08:21:10.898Z
64c94eddcb2f1bf0e7db5a4d
/avatars/f7e2532d3c85d5e5b5a02c579ea68c3a.svg
Linfeng Song
user
freesunshine0316
6773f75a23a7829936cb36c4
Qiuzhi Liu
6773f75a23a7829936cb36c5
Mengfei Zhou
6773f75a23a7829936cb36c6
Zhuosheng Zhang
6773f75a23a7829936cb36c7
Rui Wang
6773f75a23a7829936cb36c8
Zhaopeng Tu
claimed_verified
2025-05-09T07:23:20.674Z
67485743561b1e6f9579389f
/avatars/8a4cc63bd7be388010bc329bb74582a1.svg
Zhaopeng Tu
user
zptu
6773f75a23a7829936cb36c9
Haitao Mi
6773f75a23a7829936cb36ca
Dong Yu
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
The remarkable performance of models like the OpenAI o1 can be attributed to
their ability to emulate human-like long-time thinking during inference. These
models employ extended chain-of-thought (CoT) processes, exploring multiple
strategies to enhance problem-solving capabilities. However, a critical
question remains: How to intelligently and efficiently scale computational
resources during testing. This paper presents the first comprehensive study on
the prevalent issue of overthinking in these models, where excessive
computational resources are allocated for simple problems with minimal benefit.
We introduce novel efficiency metrics from both outcome and process
perspectives to evaluate the rational use of computational resources by o1-like
models. Using a self-training paradigm, we propose strategies to mitigate
overthinking, streamlining reasoning processes without compromising accuracy.
Experimental results show that our approach successfully reduces computational
overhead while preserving model performance across a range of testsets with
varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME.
The paper addresses overthinking in models like OpenAI o1 by introducing efficiency metrics and strategies to reduce computational overhead without sacrificing accuracy.
chain-of-thought overthinking self-training paradigm computational overhead accuracy