title: The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
    Agentic Tasks
summary: 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.
paper_authors._id: 67b078cb1c879c0cbb785d5f
paper_authors.name: Alejandro Cuadron
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:33:36.741Z
paper_authors._id: 652a656d1a3250bbfe3bb92d
paper_authors.avatarUrl: /avatars/a1c25150d55c493edd9a7f81287fc449.svg
paper_authors.fullname: Alejandro Cuadron Lafuente
paper_authors.type: user
paper_authors.user: AlexCuadron
paper_authors._id: 67b078cb1c879c0cbb785d60
paper_authors.name: Dacheng Li
paper_authors._id: 67b078cb1c879c0cbb785d61
paper_authors.name: Wenjie Ma
paper_authors._id: 67b078cb1c879c0cbb785d62
paper_authors.name: Xingyao Wang
paper_authors._id: 67b078cb1c879c0cbb785d63
paper_authors.name: Yichuan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:33:40.048Z
paper_authors._id: 626e3449e7914f0d5ea78ad1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/626e3449e7914f0d5ea78ad1/pVzdmdPMpNcxuj94qiIvB.jpeg
paper_authors.fullname: Yichuan
paper_authors.type: user
paper_authors.user: Chrisyichuan
paper_authors._id: 67b078cb1c879c0cbb785d64
paper_authors.name: Siyuan Zhuang
paper_authors._id: 67b078cb1c879c0cbb785d65
paper_authors.name: Shu Liu
paper_authors._id: 67b078cb1c879c0cbb785d66
paper_authors.name: Luis Gaspar Schroeder
paper_authors._id: 67b078cb1c879c0cbb785d67
paper_authors.name: Tian Xia
paper_authors._id: 67b078cb1c879c0cbb785d68
paper_authors.name: Huanzhi Mao
paper_authors._id: 67b078cb1c879c0cbb785d69
paper_authors.name: Nicholas Thumiger
paper_authors._id: 67b078cb1c879c0cbb785d6a
paper_authors.name: Aditya Desai
paper_authors._id: 67b078cb1c879c0cbb785d6b
paper_authors.name: Ion Stoica
paper_authors._id: 67b078cb1c879c0cbb785d6c
paper_authors.name: Ana Klimovic
paper_authors._id: 67b078cb1c879c0cbb785d6d
paper_authors.name: Graham Neubig
paper_authors._id: 67b078cb1c879c0cbb785d6e
paper_authors.name: Joseph E. Gonzalez
paper_title: The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
    Agentic Tasks
paper_summary: 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.
paper_ai_keywords: Reasoning Models (LRMs) overthinking Analysis Paralysis Rogue during Premature Disengagement SWE Bench Verified inference overthinking scores multimodal representation function-calling capabilities selective reinforcement learning