@inproceedings{guo-etal-2026-e3,
    title = "$E^3$-{TIR}: Enhanced Experience Exploitation for Tool-Integrated Reasoning",
    author = "Guo, Weiyang  and
      Shi, Zesheng  and
      Zhao, Liye  and
      Ma, Jiayuan  and
      Zhu, Zeen  and
      He, Junxian  and
      Zhang, Min  and
      Li, Jing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1229/",
    pages = "24575--24596",
    ISBN = "979-8-89176-395-1",
    abstract = "While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suffers from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. To address these challenges, we propose E3-TIR (Enhanced Experience Exploitation), a warm-up paradigm for the early stages of agent training. Specifically, we formulate training as the dynamic integration of three experience types: Expert Prefixes, Expert Guided, and Self-Exploration. By executing diverse branching exploration around expert ``anchors'' and employing a mix policy optimization mechanism, we effectively mitigate distribution shifts and resolve optimization conflicts arising from shared prefixes. Our method dynamically adapts the model{'}s knowledge boundaries, effectively balancing exploration diversity with training efficiency. Experimental results demonstrate that E3-TIR achieves a 6{\%} performance improvement over traditional paradigms on tool-use tasks, while requiring less than 10{\%} of the synthetic data. Furthermore, in terms of ROI{---}a comprehensive metric integrating performance, data cost, and training efficiency{---}we achieve a 1.46 gain compared to baselines."
}