Impact of SQL-based executable pipeline on cross-domain generalization

Ascertain the impact of the SQL-based executable tool-execution data generation pipeline—where tools are mapped to real relational database operations—on cross-domain generalization performance of large reasoning models trained for multi-turn, tool-mediated dialogue, determining whether execution-grounded supervision enhances or limits generalization beyond the source domains.

Background

The paper introduces a user-oriented multi-turn dialogue generation framework that integrates tool execution grounded in real relational databases, aiming to produce high-fidelity, verifiable trajectories for training agentic reasoning models. This SQL-backed pipeline maps domain-specific tools to executable SQL queries, enabling realistic state tracking and interaction.

While experimental results show improvements in certain domains (e.g., Telecom within the τ2 benchmark), the authors explicitly acknowledge uncertainty about whether benefits from execution-grounded supervision generalize across domains. They note that scalability and realism introduce complexities such as environment coupling and brittleness under partial database visibility, motivating a focused investigation into cross-domain generalization effects.