New Zero-Shot Text-to-SQL Framework Achieves State-of-the-Art Performance by Learning from Failures
Researchers have proposed Progress-SQL, a multi-turn reinforcement learning framework designed to improve large language models' ability to convert natural language into SQL queries. The system introduces an Oracle-guided Diagnostic Tree (ODT) that analyzes SQL at the clause level and provides structured feedback for iterative correction, combined with multiple reward signals measuring improvement over time. The work addresses a key limitation of existing approaches, which rely on single-shot rewards that fail to capture the value of incremental SQL refinement.
Progress-SQL is a newly proposed reinforcement learning framework aimed at enhancing Text-to-SQL generation in large language models (LLMs). Unlike prior methods that assign rewards based on a single SQL output state, Progress-SQL operates across multiple turns, rewarding measurable improvement from an initial SQL attempt to a final corrected version. Central to the approach is the Oracle-guided Diagnostic Tree (ODT), which abstracts SQL queries into clause-level structural profiles and generates diagnostic feedback to guide subsequent refinement steps. The framework combines ODT-based structural alignment with lexical alignment to produce dense, robust reward signals, and supplements these with a progression latency reward—favoring earlier correctness—and an execution status reward that incentivizes recovery from syntactically or semantically invalid SQL. Experiments conducted on the BIRD, Spider, and Spider robustness benchmark datasets show consistent performance improvements over baseline methods in both standard and robustness evaluation settings. The paper was submitted to arXiv on June 5, 2026, under the Computation and Language and Artificial Intelligence subject areas.
What's missing
The paper does not discuss computational cost or inference latency introduced by the multi-turn refinement process. It is also unclear how the framework performs on domains or database schemas not represented in BIRD or Spider.
What different sources said
- arXiv cs.CLCenter
Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
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