@inproceedings{wang-etal-2025-nat,
    title = "{NAT}: Enhancing Agent Tuning with Negative Samples",
    author = "Wang, Renxi  and
      Han, Xudong  and
      Zhang, Yixuan  and
      Baldwin, Timothy  and
      Li, Haonan",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-long.378/",
    pages = "7385--7398",
    ISBN = "979-8-89176-189-6",
    abstract = "Interaction trajectories between agents and environments have proven effective in tuning LLMs into task-specific agents. However, constructing these trajectories, especially successful trajectories, is often computationally and time intensive due to the relatively low success rates of even the most advanced LLMs, such as GPT-4 and Claude. Additionally, common training paradigms like supervised fine-tuning (SFT) and reinforcement learning (RL) not only require large volumes of data but also have specific demands regarding the trajectories used. For instance, existing SFT approaches typically utilize only positive examples, limiting their efficiency in low-resource scenarios. To address this, we introduce Negative-Aware Training (NAT), a straightforward yet effective method that leverages both successful and failed trajectories for fine-tuning, maximizing the utility of limited resources. Experimental results demonstrate that NAT consistently surpasses existing methods, including SFT, DPO, and PPO, across various tasks."
}