Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL

Understand

Large Language Models (LLMs) can translate natural language into SQL, but small models struggle with multi-table and complex queries in Zero-Shot Learning (ZSL) settings.

• While Supervised Fine-Tuning (SFT) helps, it falls short for harder cases.
• To address this, we study how different reasoning strategies (general-purpose reasoning in ZSL, reasoning traces in SFT, and Reinforcement Learning with Verifiable Reward (RLVR) with novel reward functions) affect Text2SQL performance across four benchmarks.
• We show that partial scoring rewards, computed via SQL execution, are crucial for guiding models even when outputs are not fully correct.