@inproceedings{ionov-etal-2026-call,
    title = "Call, Reward, Repeat: Advancing Dialog State Tracking with {GRPO} and Function Calling",
    author = "Ionov, Timur  and
      Marshalova, Anna  and
      Malykh, Valentin",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.21/",
    pages = "292--303",
    ISBN = "979-8-89176-383-8",
    abstract = "Recent advancements in Large Language Models (LLMs) have notably enhanced task-oriented dialogue systems, particularly in Dialogue State Tracking (DST), owing to their generative capabilities and strong generalization. Although recent approaches such as LDST and FnCTOD significantly improved cross-domain DST performance via supervised fine-tuning (SFT), these methods typically require substantial amounts of domain-specific data. In this paper, we address this limitation by employing Group Relative Policy Optimization (GRPO) - a critic-free reinforcement learning method that efficiently guides LLMs toward improved DST accuracy even under low-resource conditions. Our results on established DST benchmarks, including MultiWOZ 2.1 and 2.4, demonstrate that the RL approach achieves superior performance to existing methods while using significantly reduced out-of-domain training data. In addition, we found out that models pretrained specifically for tool-use tasks can be a better starting point, especially on small scales."
}