@inproceedings{li-etal-2026-rethinking,
    title = "Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents",
    author = "Li, Zeping  and
      Wang, Hongru  and
      Zhao, Yiwen  and
      Chen, Guanhua  and
      Li, Yixia  and
      Chen, Keyang  and
      Cao, Yixin  and
      Ye, Guangnan  and
      Chai, Hongfeng  and
      Yin, Zhenfei",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1288/",
    pages = "27955--27967",
    ISBN = "979-8-89176-390-6",
    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."
}