Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task

• URL: http://arxiv.org/abs/2506.11986v1
• Date: Fri, 13 Jun 2025 17:46:02 GMT
• Title: Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
• Authors: Wuzhenghong Wen, Su Pan, yuwei Sun,
• Abstract summary: Current fine-tuning approaches for schema linking models employ a rote-learning paradigm.<n>We propose-R1, a reasoning schema linking model trained using reinforcement learning.
• Score: 3.686808512438363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Schema linking is a critical step in Text-to-SQL task, aiming to accurately predict the table names and column names required for the SQL query based on the given question. However, current fine-tuning approaches for schema linking models employ a rote-learning paradigm, excessively optimizing for ground truth schema linking outcomes while compromising reasoning ability. This limitation arises because of the difficulty in acquiring a high-quality reasoning sample for downstream tasks. To address this, we propose Schema-R1, a reasoning schema linking model trained using reinforcement learning. Specifically, Schema-R1 consists of three key steps: constructing small batches of high-quality reasoning samples, supervised fine-tuning for cold-start initialization, and rule-based reinforcement learning training. The final results demonstrate that our method effectively enhances the reasoning ability of the schema linking model, achieving a 10\% improvement in filter accuracy compared to the existing method. Our code is available at https://github.com/hongWin/Schema-R1/.

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