MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910141193912320 |
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| author | Guo, Taicheng Wang, Hai Liu, ChaoChun Golalikhani, Mohsen Chen, Xin Zhang, Xiangliang Reddy, Chandan K. |
| author_facet | Guo, Taicheng Wang, Hai Liu, ChaoChun Golalikhani, Mohsen Chen, Xin Zhang, Xiangliang Reddy, Chandan K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training Guo, Taicheng Wang, Hai Liu, ChaoChun Golalikhani, Mohsen Chen, Xin Zhang, Xiangliang Reddy, Chandan K. Computation and Language Artificial Intelligence Databases Machine Learning 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. |
| title | MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training |
| topic | Computation and Language Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2510.12831 |