The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?

TL;DR

This paper investigates the pervasive phenomenon of tool overuse in LLMs, revealing its causes related to knowledge perception and reward structures, and proposes strategies to mitigate unnecessary tool use while improving accuracy.

Contribution

It introduces the concept of knowledge epistemic illusion and demonstrates how reward design influences tool overuse, offering effective mitigation methods with empirical validation.

Findings

Knowledge-aware boundary alignment reduces tool use by 82.8% and improves accuracy.

Balancing reward signals cuts unnecessary tool calls by over 60% without accuracy loss.

Tool overuse is linked to models' misjudgment of internal knowledge and reward incentives.

Abstract

Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecessary tool-use during reasoning. In this paper, we first reveal this phenomenon is pervasive across diverse LLMs. We then experimentally elucidate its underlying mechanisms through two key lenses: (1) First, by analyzing tool-use behavior across different internal knowledge availability regions, we identify a \textit{knowledge epistemic illusion}: models misjudge internal knowledge boundaries and fail to accurately perceive their actual knowledge availability. To mitigate this, we propose a knowledge-aware epistemic boundary alignment strategy based on direct preference optimization, which reduces tool usage in by 82.8\% while yielding an accuracy improvement. (2) Second, we establish a causal link between reward structures and tool-use behavior by visualizing the tool-augmented training process. It reveals that \textit{outcome-only rewards} inadvertently encourage tool overuse by rewarding only final correctness, regardless of tool efficiency. To verify this, we balance reward signals during training rather than relying on outcome-only rewards, cutting unnecessary tool calls by 66.7\% (7B) and 60.7\% (32B) without sacrificing accuracy. Finally, we provide theoretical justification in this two lenses to understand tool overuse.