SQL-Zero: Self-Evolving Text-to-SQL
Training a competitive Text-to-SQL agent usually depends on human-annotated natural-language/SQL pairs, which are expensive, domain-specific, and a bottleneck for scaling to new databases. We show it is possible to train a competitive solver with zero annotated pairs. We introduce SQL-Zero, a proposer-solver self-play in which a challenger and a solver start from the same base LLM and the only ground truth is execution against the database itself. The challenger generates SQL pairs calibrated to the solver's current difficulty (targeting "hard but solvable"), and both roles are updated with GRPO in alternating turns, with a template-level repetition penalty on the challenger to prevent di...
| Authors | Daniel Machado Pedrozo, Julia Soares Dollis, Bryan Lincoln Marques de Oliveira et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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