EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL

2026-06-04

AI Summary

EvoSQL通过生成器和评论家的协同进化及记忆增强，解决了复杂数据库查询的多步分解、执行诊断和针对性修正问题。

Abstract

Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction. We present EvoSQL, a co-evolution framework that formulates SQL synthesis as an iterative interaction between a generator and a critic. EvoSQL maintains a contextualized candidate memory, verifies SQL candidates with both execution signals and LLM-based critique, and updates its memory through utility-guided aggregation. To strengthen the underlying generator-critic pair, we further introduce a Self-Distillation Policy Optimization (SDPO) fine-tuning stage that injects execution-aware supervision into modern coding LLM backbones. Experiments on Spider and BIRD show that EvoSQL consistently improves open-source models over Maj@16 baselines, with particularly large gains on BIRD-Dev, ranging from +1.37% for Qwen3-4B to +9.19% for Qwen2.5-Coder-3B. SDPO initialization further improves selected backbones on Spider-Test and BIRD-Dev. These results suggest that memory-grounded co-evolution is an effective path toward more reliable and generalizable Text-to-SQL systems. Code is available at https://github.com/valleysprings/EvoSQL.

Problem

• Text-to-SQL
• complex database queries
• multi-step decomposition
• execution-based diagnosis
• targeted correction

Innovation

• co-evolution framework
• contextualized candidate memory
• Self-Distillation Policy Optimization (SDPO)