DualSQL: 基于多智能体强化学习的文本到SQL生成

中文摘要 AI 辅助

最先进的文本到SQL系统通常是围绕两个基本任务构建的多智能体流水线：模式链接和SQL生成。然而，现有工作为每个任务训练单独的模型，未能利用这些相互关联任务之间的协同效应。在本工作中，我们提出了DualSQL，一种新的文本到SQL系统，由两个智能体组成，它们共享同一个模型骨干。这两个智能体共享相同的模型权重和智能体框架，通过一个稳健的多智能体强化学习（RL）框架实现联合优化。我们设计了三个数据库访问工具，以促进基于数据库交互的多步推理。为了改进训练并避免模型崩溃，我们引入了一组rollout护栏机制，稳定多智能体RL训练，支持DualSQL在训练过程中持续改进。我们还引入了一个新的SQL正确性度量——稳健执行匹配（REX），以更准确地判断SQL正确性并分配奖励信号。仅使用3755个示例进行训练，DualSQL-4B在BIRD开发集上达到了68.0%的执行准确率，与之前的7B模型相当。DualSQL-8B进一步提高到71.1%，超过了之前具有32B参数的最先进单模型解决方案。这些结果证明了联合多智能体强化学习在构建高性能文本到SQL流水线方面的优势。

英文摘要

State-of-the-art Text-to-SQL systems are typically multi-agent pipelines centered around two fundamental tasks: schema linking and SQL generation. However, existing work trains separate models for each task, failing to leverage the synergy between these interrelated tasks. In this work, we propose DualSQL, a new Text-to-SQL system consisting of two agents powered by a single model backbone. The agents share the same model weights and agentic scaffold, enabling joint optimization through a robust multi-agent reinforcement learning (RL) framework. We design three database access tools to facilitate effective multi-step reasoning grounded to interactions with the databases. To improve training and avoid model collapse, we introduce a set of rollout guardrail mechanisms that stabilizes multi-agent RL training, supporting DualSQL to keep improving during training. We also introduce a new SQL correctness metric, robust execution match (REX), to more accurately judge SQL correctness and assign reward signals. Being trained on only 3755 examples, DualSQL-4B achieves an impressive 68.0% execution accuracy on the BIRD development set, matching previous 7B models. DualSQL-8B further improves to 71.1%, outperforming previous state-of-the-art single-model solutions with 32B parameters. These results demonstrate the strength of joint multi-agent reinforcement learning for building high performance Text-to-SQL pipelines.

发表机构

• The Ohio State University（俄亥俄州立大学）
• Google LLC（谷歌有限责任公司）

机构由 AI 辅助整理，请以论文原文为准。