Research Paper#Text-to-SQL, Reinforcement Learning, Data Synthesis🔬 ResearchAnalyzed: Jan 3, 2026 18:56
AGRO-SQL: Agentic RL for Text-to-SQL
Published:Dec 29, 2025 10:49
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
- •Proposes AGRO-SQL, a novel framework for Text-to-SQL.
- •Employs a dual-centric approach: data-centric (data synthesis) and model-centric (agentic RL).
- •Introduces a Diversity-Aware Cold Start and Group Relative Policy Optimization (GRPO) for the RL agent.
- •Achieves state-of-the-art performance on BIRD and Spider benchmarks.
Reference
“The synergistic approach achieves state-of-the-art performance among single-model methods.”