ReToolSQL Uses Agentic Reinforcement Learning for Robust Text-to-SQL

Summary

ReToolSQL combines supervised training on verified reasoning traces with agentic reinforcement fine-tuning over multi-turn tool-use trajectories. On BIRD-SQL, its Gemma 4 31B model achieves 74.32% single-pass execution accuracy and 74.77% with self-consistency.