Test-Time Verification for Text-to-SQL via Outcome Reward Models

2026-06-29

AI Summary

Existing test-time strategies for Text-to-SQL, such as Best-of-N and majority voting, rely on heuristic signals like execution success or output frequency, lacking semantic discrimination. This work proposes GradeSQL, the first framework to systematically apply outcome reward models (ORMs) to structured query generation. GradeSQL introduces a scalable, human-annotation-free ORM training method by automatically generating candidate SQL queries and labeling them based on execution outcomes, then integrates the trained ORM into a verification-driven Best-of-N selection mechanism. Experimental results demonstrate that GradeSQL achieves performance gains of 4.33% and 2.10% on the BIRD and Spider benchmarks, respectively, with particularly pronounced advantages on complex queries and large candidate sets.

Abstract

Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference strategies, including Best-of-N sampling and Majority Voting, rely on heuristic signals such as execution success or output frequency, which provide limited semantic discrimination across candidate outputs. In this work, we study Outcome Reward Models (ORMs) as learned semantic scoring functions for test-time verification in Text-to-SQL. While ORMs have been previously explored for test-time scaling and alignment, their application to structured query generation remains underexplored. We introduce GradeSQL, a scalable framework for training task-specific ORMs via automated candidate generation and execution-based labeling, enabling verifier training without manual annotation. We integrate ORMs into a verification-driven Best-of-N pipeline and evaluate our approach on the BIRD and Spider benchmarks across multiple open-source LLM families. ORM-based selection consistently outperforms execution-based Best-of-N and Majority Voting, with gains of up to +4.33% on BIRD and +2.10% on Spider. We further show that ORMs scale effectively with larger candidate sets and yield stronger improvements on complex queries. Overall, our results demonstrate that ORM-based verification provides a simple, effective, and scalable alternative to heuristic test-time selection strategies for Text-to-SQL. Code datasets and models are publicly available.

Problem

• Text-to-SQL
• test-time verification
• Outcome Reward Models
• structured reasoning
• semantic scoring

Innovation

• Outcome Reward Models
• Text-to-SQL
• test-time verification
• GradeSQL
• structured reasoning