Abstract
Recent advancements in Large Language Models (LLMs) have notably enhanced taskoriented 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.