The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
  Agentic Tasks
Authors: name: Alejandro Cuadron, name: Dacheng Li, name: Wenjie Ma, name: Xingyao Wang, name: Yichuan Wang, name: Siyuan Zhuang, name: Shu Liu, name: Luis Gaspar Schroeder, name: Tian Xia, name: Huanzhi Mao, name: Nicholas Thumiger, name: Aditya Desai, name: Ion Stoica, name: Ana Klimovic, name: Graham Neubig, name: Joseph E. Gonzalez

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.