Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
Quick Answer
Progress-SQL enhances Text-to-SQL generation by introducing a multi-turn reinforcement learning framework with progressive rewards, improving performance on benchmarks like BIRD and Spider.
Quick Take
The method employs an Oracle-guided Diagnostic Tree for structured feedback and combines multiple reward signals, leading to consistent performance gains in both primary and robustness evaluations.
Key Points
- Introduces Oracle-guided Diagnostic Tree for clause-level SQL abstraction.
- Combines structural and lexical alignment for dense reward signals.
- Implements progressive rewards to measure SQL improvement iteratively.
- Demonstrates consistent performance gains on BIRD and Spider benchmarks.
- Incorporates latency and execution status rewards for enhanced SQL correction.
Paper Resources
Source Excerpt
arXiv:2606. 06825v1 Announce Type: new Abstract: Reinforcement learning has recently shown promise in improving for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined over a single SQL state. Such rewards provide limited guidance for iterative SQL correction and are insufficient to capture the improvement of multi-turn SQL refinement.
In this paper, we propose Progress-SQL, a multi-turn reinforcement learning framework with progressive rewards for Text-to-SQL. …
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