Paper page - R^3-SQL: Ranking Reward and Resampling for Text-to-SQL

Source: https://huggingface.co/papers/2604.25325

Abstract

R^3-SQL addresses inconsistencies in scoring functionally equivalent SQL queries and improves candidate recall through unified reward ranking and agentic resampling techniques.

ModernText-to-SQLsystems generate multiple candidateSQL queriesand rank them to judge a final prediction. However, existing methods face two limitations. First, they often score functionally equivalentSQL queriesinconsistently despite identical execution results. Second, ranking cannot recover when the correct SQL is absent from the candidate pool. We propose R^3-SQL, aText-to-SQLframework that addresses both issues through unified reward for ranking and resampling. R^3-SQL first groups candidates by execution result and ranks groups for consistency. To score each group, it combines apairwise preferenceacross groups with apointwise utilityfrom the best group rank and size, capturing relative preference, consistency, and candidate quality. To improve candidate recall, R^3-SQL introducesagentic resampling, which judges the generated candidate pool and selectively resamples when the correct SQL is likely absent. R^3-SQL achieves 75.03execution accuracyon BIRD-dev, a new state of the art among methods using models with disclosed sizes, with consistent gains across five benchmarks.

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