@inproceedings{guo-etal-2026-mtsql,
title = "{MTSQL}-R1: Towards Long-Horizon Multi-Turn Text-to-{SQL} via Agentic Training",
author = "Guo, Taicheng and
Wang, Hai and
Liu, Chaochun and
Golalikhani, Mohsen and
Chen, Xin and
Zhang, Xiangliang and
Reddy, Chandan K.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1563/",
pages = "33905--33938",
ISBN = "979-8-89176-390-6",
abstract = "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-{\ensuremath{>}}execute-{\ensuremath{>}}verify-{\ensuremath{>}}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, reasoning trajectories, etc.) will be released upon acceptance to contribute to community research."
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<abstract>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-\ensuremath>execute-\ensuremath>verify-\ensuremath>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, reasoning trajectories, etc.) will be released upon acceptance to contribute to community research.</abstract>
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%0 Conference Proceedings
%T MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training
%A Guo, Taicheng
%A Wang, Hai
%A Liu, Chaochun
%A Golalikhani, Mohsen
%A Chen, Xin
%A Zhang, Xiangliang
%A Reddy, Chandan K.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F guo-etal-2026-mtsql
%X 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-\ensuremath>execute-\ensuremath>verify-\ensuremath>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, reasoning trajectories, etc.) will be released upon acceptance to contribute to community research.
%U https://aclanthology.org/2026.acl-long.1563/
%P 33905-33938
Markdown (Informal)
[MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training](https://aclanthology.org/2026.acl-long.1563/) (Guo et al., ACL 2026)
ACL
- Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, and Chandan K. Reddy. 2026. MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33905–33938, San Diego, California, United States. Association for Computational Linguistics.