@inproceedings{zhang-etal-2026-looptool,
    title = "{L}oop{T}ool: Closing the Data{--}Training Loop for Robust {LLM} Tool Calls",
    author = "Zhang, Kangning  and
      Liu, Weiwen  and
      Jiao, Wenxiang  and
      Du, Kounianhua  and
      Lu, Yuan  and
      Zhang, Weinan  and
      Yu, Yong",
    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.968/",
    pages = "21144--21164",
    ISBN = "979-8-89176-390-6",
    abstract = "Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines, where data generation and model training are executed as two separate, non-interactive processes. This approach fails to focus on the model{'}s specific weaknesses adaptively and allows noisy labels to persist, degrading training efficiency. We introduce \textbf{LoopTool}, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively evolves both the data and the model through three synergistic modules: (1) \textit{Greedy Capability Probing (GCP)} diagnoses the model{'}s mastered and failed capabilities; (2) \textit{Judgement-Guided Label Verification (JGLV)} uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) \textit{Error-Driven Data Expansion (EDDE)} generates new, challenging samples based on identified failures. This closed-loop process is tightly integrated with reinforcement learning training and operates within a cost-efficient, open-source ecosystem, thereby eliminating reliance on costly APIs. Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs."
}