MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic
  Training
Authors: name: Taicheng Guo, name: Hai Wang, name: ChaoChun Liu, name: Mohsen Golalikhani, name: Xin Chen, name: Xiangliang Zhang, name: Chandan K. Reddy
Organization: Amazon

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.