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[2504.15077] Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL
Summary
The paper 'Think2SQL' explores enhancing reasoning capabilities in Text-to-SQL tasks using Reinforcement Learning with Verifiable Rewards, revealing critical insights for model optimization.
Why It Matters
As large language models (LLMs) become integral in database querying, improving their reasoning capabilities is essential for effective data interaction. This research addresses existing limitations in multi-table environments, providing a framework for future advancements in Text-to-SQL applications.
Key Takeaways
- Introduces a novel execution-guided dense reward function for improved feedback.
- Demonstrates that model size influences the effectiveness of reward strategies.
- Evaluates the impact of cold starts on model performance and training efficiency.
Computer Science > Machine Learning arXiv:2504.15077 (cs) [Submitted on 21 Apr 2025 (v1), last revised 23 Feb 2026 (this version, v4)] Title:Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL Authors:Simone Papicchio, Simone Rossi, Luca Cagliero, Paolo Papotti View a PDF of the paper titled Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL, by Simone Papicchio and Simone Rossi and Luca Cagliero and Paolo Papotti View PDF HTML (experimental) Abstract:While Large Language Models (LLMs) have advanced the state-of-the-art in Text-to-SQL, robust reasoning in complex, multi-table environments remains a bottleneck for parameter-efficient models. This paper presents a systematic empirical study on injecting reasoning capabilities into Text-to-SQL through the lens of Reinforcement Learning with Verifiable Rewards (RLVR). We uncover a critical interplay between reward density, advantage scaling, and model capacity. Our analysis yields four primary insights. First, we propose a novel execution-guided dense reward function that significantly outperforms binary signals and existing state-of-the-art rewards by providing granular feedback at the instance level. Second, we analyze the mechanics of advantage calculation, demonstrating that while large models thrive on sparse signals with aggressive advantage scaling, smaller models require dense rewards and conservative scaling to improve Text-to-SQL performance. Third, we evaluate the impact of cold start, showing tha...
