The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

2025-02-12

AI Summary

This paper identifies the pervasive “overthinking” phenomenon in Large Reasoning Models (LRMs) during interactive tasks—characterized by excessive reliance on lengthy internal reasoning while neglecting environmental feedback, thereby degrading efficiency. We formally define and quantify overthinking via a novel, interpretable evaluation framework, revealing three canonical behavioral patterns: analytical paralysis, uncontrolled action sequences, and premature termination. Leveraging the SWE-Bench Verified benchmark, we conduct trajectory analysis calibrated by human experts, exploit native function-calling capabilities, and apply selective reinforcement learning. Empirical results demonstrate that reducing overthinking scores improves task success by nearly 30% and cuts computational overhead by 43%. To foster reproducible research, we open-source both the evaluation framework and a high-quality dataset comprising 4,018 interactive trajectories—establishing a new paradigm and foundational resource for efficient, embodied decision-making in LRM-based agents.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Examines overthinking in Large Reasoning Models.
• Proposes framework to mitigate overthinking in AI.
• Improves performance by reducing computational costs.

Innovation

• Mitigates overthinking in LRMs
• Uses selective reinforcement learning
• Reduces computational costs significantly