SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL
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arXiv
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| Format: | Preprint |
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2025
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| author | Lyu, Shuai Luo, Haoran Li, Ripeng Ou, Zhonghong Sun, Jiangfeng Qin, Yang Shang, Xiaoran Song, Meina Zhu, Yifan |
| author_facet | Lyu, Shuai Luo, Haoran Li, Ripeng Ou, Zhonghong Sun, Jiangfeng Qin, Yang Shang, Xiaoran Song, Meina Zhu, Yifan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL Lyu, Shuai Luo, Haoran Li, Ripeng Ou, Zhonghong Sun, Jiangfeng Qin, Yang Shang, Xiaoran Song, Meina Zhu, Yifan Databases Artificial Intelligence 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. |
| title | SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2502.11741 |