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Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL
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Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation. Existing approaches often rely on handcrafted reasoning paths with inductive biases that can limit their overall effectiveness. Motivated by the recent success of reasoning-enhanced models such as DeepSeek R1 and OpenAI o1, which effectively leverage reward-driven self-exploration to enhance reasoning capabilities and generalization, we propose a novel set of partial rewards tailored specifically for the Text-to-SQL task. Our reward set includes schema-linking, AI feedback, n-gram similarity, and syntax check, explicitly designed to address the reward sparsity issue prevalent in reinforcement learning (RL). Leveraging group relative policy optimization (GRPO), our approach explicitly encourages large language models (LLMs) to develop intrinsic reasoning skills necessary for accurate SQL query generation. With models of different sizes, we demonstrate that RL-only training with our proposed rewards consistently achieves higher accuracy and superior generalization compared to supervised fine-tuning (SFT). Remarkably, our RL-trained 14B-parameter model significantly outperforms larger proprietary models, e.g. o3-mini by 4% and Gemini-1.5-Pro-002 by 3% on the BIRD benchmark. These highlight the efficacy of our proposed RL-training framework with partial rewards for enhancing both accuracy and reasoning capabilities in Text-to-SQL tasks.
Forward citations
Cited by 6 Pith papers
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Reliability annotation for tabular QA reaches near-perfect accuracy with about 200 schema-stratified SFT examples, and GRPO adds measurable value only when SFT is undertrained.
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SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL
SQLens uses weak supervision over database and LLM error signals to detect clause-level semantic errors in text-to-SQL and fixes them iteratively, improving execution accuracy on BIRD and Spider.
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RAISE: Reasoning Agent for Interactive SQL Exploration
An interactive reasoning agent that explores databases with live queries raises text-to-SQL execution accuracy from 44.8% to 56.5% and achieves 81.8% best-of-N coverage on BIRD.
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PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning
PaVeRL-SQL reports SOTA execution accuracy on Spider2.0-SQLite using partial-match rewards and verbal RL, but overclaims SOTA on Spider and BIRD.
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LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning
The authors built a 4,038-question text-to-SQL benchmark with chain-of-thought annotations and found that state-of-the-art models reach only 33.20% execution accuracy.
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CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
A 7B model trained with GRPO and a sparse execution-correctness reward reaches 59.97% execution accuracy on BIRD dev, though the evaluation protocol and baseline numbers contain inconsistencies.
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