SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL

• URL: http://arxiv.org/abs/2502.11741v3
• Date: Thu, 22 May 2025 15:06:30 GMT
• Title: SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL
• Authors: Shuai Lyu, Haoran Luo, Ripeng Li, Zhonghong Ou, Jiangfeng Sun, Yang Qin, Xiaoran Shang, Meina Song, Yifan Zhu,
• Abstract summary: SQL-o1 is a self-reward-driven search framework built on an agent-based architecture to enhance model reasoning capabilities.<n>It achieves a +10.8 execution accuracy improvement on the complex Bird dataset, surpassing even GPT-4-based models.<n>It exhibits strong few-shot generalization and robust cross-model transferability across open-source LLMs.
• Score: 10.82260429602196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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