ACE-SQL jointly optimizes schema linking and SQL generation via RL with empirical credit assignment from execution-correct rollouts, achieving 65.3% greedy execution accuracy on BIRD Dev using 0.93k output tokens.
arXiv preprint arXiv:2601.17699 , year=
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Progress-SQL introduces a multi-turn RL framework with ODT-based structural alignment and progressive rewards that measure improvement across refinement turns, yielding gains on BIRD, Spider, and robustness benchmarks.
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ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL
ACE-SQL jointly optimizes schema linking and SQL generation via RL with empirical credit assignment from execution-correct rollouts, achieving 65.3% greedy execution accuracy on BIRD Dev using 0.93k output tokens.
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Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
Progress-SQL introduces a multi-turn RL framework with ODT-based structural alignment and progressive rewards that measure improvement across refinement turns, yielding gains on BIRD, Spider, and robustness benchmarks.