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Human-level Text-to-SQL achieved with verified data and RL

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for Text-to-SQL that achieves human-level accuracy by fine-tuning LLMs with reinforcement learning on verified data, bypassing complex pipeline engineering. They created BIRD-Platinum, a dataset with corrected annotations, which improved Qwen3-235B performance significantly. Further enhancements were made with ReViSQL-BIRD, a reward shaping technique that combines result-based rewards with SQL equivalence verification and external knowledge utilization, enabling Kimi-K2.6 to reach 92.96% accuracy on the Arcwise-Plat benchmark. AI

IMPACT This research demonstrates a path to human-level performance in Text-to-SQL without complex pipelines, potentially simplifying database interaction for users.

RANK_REASON Academic paper detailing a new method and benchmark results for Text-to-SQL. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Human-level Text-to-SQL achieved with verified data and RL

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Academic paper detailing a new method and benchmark results for Text-to-SQL. [lever_c_demoted from research: ic=1 ai=1.0]
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46 days old
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yuxuan Zhu, Tengjun Jin, Yoojin Choi, Daniel Kang ·

    Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering

    arXiv:2603.20004v3 Announce Type: replace-cross Abstract: Translating natural language questions to SQL queries (Text-to-SQL) is a long-standing problem in database research. Recent efforts have focused on improving accuracy by building increasingly complex multi-stage large LLM …

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