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

Shuai Lyu, Haoran Luo, Ripeng Li, Zhonghong Ou, Jiangfeng Sun, Yang Qin, Xiaoran Shang, Meina Song, Yifan Zhu

TL;DR

SQL-o1 is introduced, a self-reward-driven heuristic search framework built on an agent-based architecture to enhance model reasoning capabilities and exhibits strong few-shot generalization and robust cross-model transferability across open-source LLMs.

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.

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

TL;DR

SQL-o1 is introduced, a self-reward-driven heuristic search framework built on an agent-based architecture to enhance model reasoning capabilities and exhibits strong few-shot generalization and robust cross-model transferability across open-source LLMs.

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.

Paper Structure

This paper contains 48 sections, 3 theorems, 53 equations, 15 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Method: SQL-o1
  5. Agent Initialization
  6. Heuristic Dynamic Search with Self-Reward
  7. Self-Reward Evaluation
  8. Heuristic Dynamic Search
  9. Query Trajectory Optimization
  10. Cold Start and Optimization for SQL-o1 Reasoning
  11. Data Preparation
  12. MCTS Dynamic Pruning
  13. Experiments
  14. Experimental Settings
  15. Main Result
  16. ...and 33 more sections

Key Result

Proposition 1

SQL-o1's self-reward mechanism makes it more effective at generating optimal SQL compared to traditional end-to-end methods.

Figures (15)

  • Figure 1: The illustrations of the differences among end-to-end Text2SQL method and Heuristic Dynamic Search with Self-Reward.
  • Figure 2: Distributions of SQL clauses and error types across databases in Spider and Bird. While schema linking errors are common, logic and function errors are notably more frequent and persistent—highlighting the limitations of single-pass decoding in handling complex reasoning.
  • Figure 3: An overview of the implementation of the SQL-o1 framework.
  • Figure 4: An example illustrates MCTS search guided by self-reward.
  • Figure 5: CodeLlama-7B (CL-7B).
  • ...and 10 more figures

Theorems & Definitions (6)

  • proof
  • proof
  • proof
  • Proposition 1
  • Proposition 2
  • Proposition 3