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

Title: Schema-R1：Text-to-SQL任务中用于模式链接的推理训练方法

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/. Show Chinese abstract

Abstract: Schema链接是Text-to-SQL任务中的关键步骤，旨在根据给定的问题准确预测SQL查询所需的表名和列名。 然而，当前用于Schema链接模型的微调方法采用机械学习范式，过度优化真实Schema链接结果，而损害了推理能力。 这种限制源于获取高质量推理样本以供下游任务使用存在困难。 为了解决这个问题，我们提出了Schema-R1，这是一种使用强化学习训练的推理Schema链接模型。 具体来说，Schema-R1包括三个关键步骤：构建高质量推理样本的小批次、监督微调以实现冷启动初始化以及基于规则的强化学习训练。 最终结果显示，我们的方法有效提升了Schema链接模型的推理能力，在过滤准确性方面比现有方法提高了10%。 我们的代码可在https://github.com/hongWin/Schema-R1/获取。