MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic
  Training
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.
68f0566536f8b025381e1939
Taicheng Guo
claimed_verified
2025-10-16T06:38:48.648Z
60ed74f536ceac2554083559
/avatars/28c184ef76f719a720d933d05afb5800.svg
taicheng guo
user
taicheng
68f0566536f8b025381e193a
Hai Wang
68f0566536f8b025381e193b
ChaoChun Liu
68f0566536f8b025381e193c
Mohsen Golalikhani
68f0566536f8b025381e193d
Xin Chen
68f0566536f8b025381e193e
Xiangliang Zhang
68f0566536f8b025381e193f
Chandan K. Reddy
MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic
  Training
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, an agentic training framework, improves multi-turn Text-to-SQL by treating it as an MDP with iterative propose-execute-verify-refine cycles, enhancing coherence and execution.
Multi-turn Text-to-SQL Markov Decision Process (MDP) dialogue coherence dialogue memory iterative propose-execute-verify-refine cycle conversational semantic parsing