用强化学习提升文本转SQL中模式链接的推理能力
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
- 构建高质量推理样本，通过规则化强化学习训练模型
- 相比现有方法，过滤准确率提升10%
- 适合需要强逻辑推理的Text-to-SQL研究者
在文本转SQL任务中，模式链接是关键步骤，旨在根据问题准确预测所需的表名和列名。然而，当前微调方法采用机械式学习范式，过度优化真实标注结果，损害了模型的推理能力。其根本原因在于难以获取高质量的下游推理样本。为此，我们提出Schema-R1，一种基于强化学习的推理型模式链接方法。该方法包含三个核心步骤：构建小批量高质量推理样本、监督微调实现冷启动初始化、基于规则的强化学习训练。实验表明，该方法有效提升了模型的推理能力，在过滤准确率上相较现有方法提升10%。代码已开源：https://github.com/hongWin/Schema-R1/
原文摘要 · Abstract (English)
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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