AGRO-SQL: Agentic RL for Text-to-SQL

This paper addresses the limitations of Text-to-SQL systems by tackling the scarcity of high-quality training data and the reasoning challenges of existing models. It proposes a novel framework combining data synthesis and a new reinforcement learning approach. The data-centric approach focuses on creating high-quality, verified training data, while the model-centric approach introduces an agentic RL framework with a diversity-aware cold start and group relative policy optimization. The results show state-of-the-art performance, indicating a significant contribution to the field.