Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective

• URL: http://arxiv.org/abs/2510.00186v2
• Date: Thu, 02 Oct 2025 18:28:05 GMT
• Title: Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective
• Authors: Anni Li, Aria Attar, Paul Dong,
• Abstract summary: Thinkquel is a fine-tuned model for producing robust, portable, execution-validated queries.<n>TS-GRPO bridges the gap between token-level training signals and sequence-level execution rewards.
• Score: 2.5297878656953605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transforming natural-language requests into reliable, production-ready data transformations remains challenging: correctness depends on precise schema linking and warehouse-specific SQL dialects, while the strongest supervision available during training--execution success and result matching--are provided only at the sequence level. At the same time, assembling large, execution-validated corpora is costly, and token-level objectives misalign with these global signals, yielding unstable optimization and limited portability. We introduce Thinkquel, a fine-tuned model for producing robust, portable, and execution-validated database queries. Methodologies in Thinkquel integrates a novel synthetic data pipeline, TS-SQL, that leverages dbt as a portable intermediate representation with a span-aware reinforcement learning objective, and Token-Sequence GRPO (TS-GRPO), specifically designed to bridge the gap between token-level training signals and sequence-level execution rewards when finetuning LLMs. On the 500-example TS-SQL test set, Thinkquel (32B) reaches 93.2% execution success and 61.8% exact-result match with a two-stage SFT curriculum, improving over the base model by 67.2% (exec.) and 44.4% (match). In Spider (14B) experiments, TS-GRPO increases training stability and speeds convergence of the execution-match reward relative to GRPO and GSPO.

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