MTSQL-R1：通过代理训练实现长程多轮文本到SQL的转换

摘要

多轮文本转SQL旨在将用户的对话式表达转化为可执行的SQL查询，同时保持对话的连贯性并与目标数据库模式相契合。然而，现有系统大多仅将此任务视为简单的文本翻译，遵循短视范式，逐轮生成查询而不进行执行、显式验证和优化，导致输出不可执行或不连贯。我们提出了MTSQL-R1，一个面向长视域多轮文本转SQL的代理训练框架。我们将该任务建模为马尔可夫决策过程（MDP），其中代理与（i）数据库交互以获取执行反馈，（ii）持久对话记忆以进行连贯性验证，执行“提议执行->验证->优化”的迭代循环，直至所有检查通过。在COSQL和SPARC上的实验表明，MTSQL-R1持续超越强基线，凸显了环境驱动验证和记忆引导优化在对话式语义解析中的重要性。完整方案（包括代码、训练模型、日志、推理轨迹等）将在内部评审后发布，以助力社区研究。

English

Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, most existing systems only regard this task as a simple text translation task and follow a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. We present MTSQL-R1, an agentic training framework for long-horizon multi-turn Text-to-SQL. We cast the task as a Markov Decision Process (MDP) in which an agent interacts with (i) a database for execution feedback and (ii) a persistent dialogue memory for coherence verification, performing an iterative propose to execute -> verify -> refine cycle until all checks pass. Experiments on COSQL and SPARC demonstrate that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. Full recipes (including code, trained models, logs, reasoning trajectories, etc.) will be released after the internal review to contribute to community research.

MTSQL-R1：通过代理训练实现长程多轮文本到SQL的转换

MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training

摘要

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