The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
  Agentic Tasks
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving
capabilities, but their effectiveness in interactive environments can be
limited. This paper introduces and analyzes overthinking in LRMs. A phenomenon
where models favor extended internal reasoning chains over environmental
interaction. Through experiments on software engineering tasks using SWE Bench
Verified, we observe three recurring patterns: Analysis Paralysis, Rogue
Actions, and Premature Disengagement. We propose a framework to study these
behaviors, which correlates with human expert assessments, and analyze 4018
trajectories. We observe that higher overthinking scores correlate with
decreased performance, with reasoning models exhibiting stronger tendencies
toward overthinking compared to non-reasoning models. Our analysis reveals that
simple efforts to mitigate overthinking in agentic environments, such as
selecting the solution with the lower overthinking score, can improve model
performance by almost 30% while reducing computational costs by 43%. These
results suggest that mitigating overthinking has strong practical implications.
We suggest that by leveraging native function-calling capabilities and
selective reinforcement learning overthinking tendencies could be mitigated. We
also open-source our evaluation framework and dataset to facilitate research in
this direction at https://github.com/AlexCuadron/Overthinking.
67b078cb1c879c0cbb785d5f
Alejandro Cuadron
claimed_verified
2025-02-18T09:33:36.741Z
652a656d1a3250bbfe3bb92d
/avatars/a1c25150d55c493edd9a7f81287fc449.svg
Alejandro Cuadron
user
AlexCuadron
67b078cb1c879c0cbb785d60
Dacheng Li
67b078cb1c879c0cbb785d61
Wenjie Ma
67b078cb1c879c0cbb785d62
Xingyao Wang
67b078cb1c879c0cbb785d63
Yichuan Wang
claimed_verified
2025-02-18T09:33:40.048Z
626e3449e7914f0d5ea78ad1
https://cdn-avatars.huggingface.co/v1/production/uploads/626e3449e7914f0d5ea78ad1/pVzdmdPMpNcxuj94qiIvB.jpeg
Yichuan
user
Chrisyichuan
67b078cb1c879c0cbb785d64
Siyuan Zhuang
67b078cb1c879c0cbb785d65
Shu Liu
67b078cb1c879c0cbb785d66
Luis Gaspar Schroeder
67b078cb1c879c0cbb785d67
Tian Xia
67b078cb1c879c0cbb785d68
Huanzhi Mao
67b078cb1c879c0cbb785d69
Nicholas Thumiger
67b078cb1c879c0cbb785d6a
Aditya Desai
67b078cb1c879c0cbb785d6b
Ion Stoica
67b078cb1c879c0cbb785d6c
Ana Klimovic
67b078cb1c879c0cbb785d6d
Graham Neubig
67b078cb1c879c0cbb785d6e
Joseph E. Gonzalez
The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
  Agentic Tasks
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving
capabilities, but their effectiveness in interactive environments can be
limited. This paper introduces and analyzes overthinking in LRMs. A phenomenon
where models favor extended internal reasoning chains over environmental
interaction. Through experiments on software engineering tasks using SWE Bench
Verified, we observe three recurring patterns: Analysis Paralysis, Rogue
Actions, and Premature Disengagement. We propose a framework to study these
behaviors, which correlates with human expert assessments, and analyze 4018
trajectories. We observe that higher overthinking scores correlate with
decreased performance, with reasoning models exhibiting stronger tendencies
toward overthinking compared to non-reasoning models. Our analysis reveals that
simple efforts to mitigate overthinking in agentic environments, such as
selecting the solution with the lower overthinking score, can improve model
performance by almost 30% while reducing computational costs by 43%. These
results suggest that mitigating overthinking has strong practical implications.
We suggest that by leveraging native function-calling capabilities and
selective reinforcement learning overthinking tendencies could be mitigated. We
also open-source our evaluation framework and dataset to facilitate research in
this direction at https://github.com/AlexCuadron/Overthinking.
Analysis of overthinking in Large Reasoning Models shows it correlates with decreased performance, and selective mitigation techniques can improve performance and reduce costs.
Large Reasoning Models LRM overthinking environmental interaction Analysis Paralysis Rogue Actions Premature Disengagement reasoning models computational costs native function-calling capabilities selective reinforcement learning