Text-to-SQL (Text2SQL) aims to map natural language questions to executable SQL queries. Although large language models (LLMs) have driven significant progress, current approaches struggle with poor transferability to open-source LLMs, limited robustness against logic and function errors in complex queries, and inefficiencies in structured search. We introduce SQL-o1, a self-reward-driven heuristic search framework built on an agent-based architecture to enhance model reasoning capabilities. SQL-o1 leverages Monte Carlo Tree Search (MCTS) for structured, multi-step exploration, and incorporates a dynamic pruning strategy to accelerate inference without sacrificing accuracy. On the Spider and Bird benchmarks, SQL-o1 achieves a +10.8 execution accuracy improvement on the complex Bird dataset, surpassing even GPT-4-based models. Notably, it exhibits strong few-shot generalization and robust cross-model transferability across open-source LLMs. Our code is available at:https://github.com/ShuaiLyu0110/SQL-o1.
@article{arxiv.2502.11741,
title = {SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL},
author = {Shuai Lyu and Haoran Luo and Ripeng Li and Zhonghong Ou and Jiangfeng Sun and Yang Qin and Xiaoran Shang and Meina Song and Yifan Zhu},
journal= {arXiv preprint arXiv:2502.11741},
year = {2025}
}