Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents

Entropy reduction in LLM token distributions serves as a reliable supervisory signal for optimizing tool-use behavior in agents, enabling both more efficient (fewer calls) and higher-quality tool invocations via two complementary reward strategies.

Problem Statement

LLM-based tool-using agents in long trajectories tend to make excessive and low-quality tool calls, increasing latency and degrading performance. Existing approaches lack fine-grained, principled signals to guide when and how agents should invoke tools. This work addresses the absence of an intrinsic, model-level mechanism to distinguish necessary high-quality tool calls from redundant ones.

Key Novelty

Empirical discovery of a strong positive correlation between entropy reduction in LLM output distributions and high-quality tool call decisions, establishing entropy as a meaningful proxy for tool-use quality
Sparse outcome reward strategy using trajectory-level entropy-reduction signals to optimize tool-use efficiency, reducing unnecessary calls
Dense process reward strategy using fine-grained, step-level entropy-reduction supervision to improve overall task performance quality

Evaluation Highlights

Sparse outcome reward reduces tool calls by 72.07% compared to the average of baselines, dramatically improving efficiency
Dense process reward improves task performance by 22.27% over baselines, demonstrating superior fine-grained supervision across diverse domains including mathematical reasoning and multi-hop QA

Breakthrough Assessment

6/10 The paper introduces a novel and principled use of entropy as a reward signal for tool-use optimization with strong empirical results, representing a solid contribution; however, it builds on existing RL-for-LLM frameworks and the core insight, while clever, is an incremental theoretical advance rather than a paradigm shift.

Methodology

Conduct pilot experiments measuring entropy in LLM output distributions during tool-use trajectories, identifying a positive correlation between entropy reduction and high-quality tool calls
Design a sparse outcome reward that assigns trajectory-level signals based on cumulative entropy reduction, penalizing excessive or low-quality tool invocations to improve efficiency
Design a dense process reward that provides step-level entropy-reduction signals during RL training, offering fine-grained supervision to guide the agent toward high-quality tool use at each decision point

System Components

Entropy-Based Pilot Analysis

Empirical study correlating token-level entropy reduction in LLM outputs with the quality and necessity of tool calls, validating entropy as a supervisory proxy

Sparse Outcome Reward

Coarse, trajectory-level reward signal derived from entropy reduction that guides the agent to reduce unnecessary tool calls and improve efficiency

Dense Process Reward

Fine-grained, per-step reward signal based on entropy reduction that provides continuous supervision throughout the trajectory to enhance tool-call quality and task performance

RL Training Framework

Reinforcement learning pipeline that incorporates both reward strategies to optimize LLM agent policies for adaptive tool-use behavior

Results

Metric/Benchmark	Baseline (avg)	This Paper	Delta
Tool Call Frequency (efficiency)	Baseline avg tool calls	-72.07% tool calls	-72.07%
Task Performance (accuracy/F1)	Baseline avg performance	+22.27% improvement	+22.27%
Domains Covered	Math reasoning, multi-hop QA	Diverse domains	Generalized
Latency Impact	High (excessive calls)	Significantly reduced	Qualitative improvement

Key Takeaways

Entropy reduction is a practical, model-intrinsic signal that can be extracted without external annotations and used directly as a reward to train more efficient and accurate tool-using LLM agents
Practitioners should choose between sparse vs. dense entropy rewards based on their optimization target: use sparse rewards when minimizing API/tool call costs is the priority, and dense rewards when maximizing task accuracy matters most
This approach is domain-agnostic and applicable to any RL-based LLM agent training pipeline, making it a low-overhead enhancement for production agentic systems dealing with long-horizon reasoning tasks

Abstract

Tool-using agents based on Large Language Models (LLMs) excel in tasks such as mathematical reasoning and multi-hop question answering. However, in long trajectories, agents often trigger excessive and low-quality tool calls, increasing latency and degrading inference performance, making managing tool-use behavior challenging. In this work, we conduct entropy-based pilot experiments and observe a strong positive correlation between entropy reduction and high-quality tool calls. Building on this finding, we propose using entropy reduction as a supervisory signal and design two reward strategies to address the differing needs of optimizing tool-use behavior. Sparse outcome rewards provide coarse, trajectory-level guidance to improve efficiency, while dense process rewards offer fine-grained supervision to enhance performance. Experiments across diverse domains show that both reward designs improve tool-use behavior: the former reduces tool calls by 72.07% compared to the average of baselines, while the latter improves performance by 22.27%. These results position entropy reduction as a key mechanism for enhancing tool-use behavior, enabling agents to be more adaptive in real-world applications.