title: SQL-R1: Training Natural Language to SQL Reasoning Model By
    Reinforcement Learning
summary: Natural Language to SQL (NL2SQL) enables intuitive interactions with
  databases by transforming natural language queries into structured SQL
  statements. Despite recent advancements in enhancing human-computer interaction
  within database applications, significant challenges persist, particularly
  regarding the inference performance in complex scenarios involving multi-table
  joins and nested queries. Current methodologies primarily utilize supervised
  fine-tuning (SFT) to train the NL2SQL model, which may limit adaptability and
  interpretability in new environments (e.g., finance and healthcare). In order
  to enhance the reasoning performance of the NL2SQL model in the above complex
  situations, we introduce SQL-R1, a novel NL2SQL reasoning model trained by the
  reinforcement learning (RL) algorithms. We design a specialized RL-based reward
  function tailored for NL2SQL tasks and discussed the impact of cold start on
  the effectiveness of intensive training. In addition, we achieve competitive
  accuracy using only a tiny amount of synthetic NL2SQL data for augmented
  training and further explore data engineering for RL. In existing experiments,
  SQL-R1 achieves execution accuracy of 88.6% and 66.6% on the benchmark Spider
  and BIRD, respectively, only using the 7B base model.
paper_authors._id: 67fc9e72b2383c63dc413dcb
paper_authors.name: Peixian Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T08:59:56.900Z
paper_authors._id: 663e29ddc0ce43e121c80f92
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/663e29ddc0ce43e121c80f92/jEAQa2oLCRUpiFEhTy0nK.jpeg
paper_authors.fullname: Peixian Ma
paper_authors.type: user
paper_authors.user: MPX0222forHF
paper_authors._id: 67fc9e72b2383c63dc413dcc
paper_authors.name: Xialie Zhuang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-14T09:46:23.810Z
paper_authors._id: 6575a625b951d40e7a4d8685
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6575a625b951d40e7a4d8685/mcnyqU--r-vi11aXN02cZ.jpeg
paper_authors.fullname: zhuangxialie
paper_authors.type: user
paper_authors.user: ZhuangXialie
paper_authors._id: 67fc9e72b2383c63dc413dcd
paper_authors.name: Chengjin Xu
paper_authors._id: 67fc9e72b2383c63dc413dce
paper_authors.name: Xuhui Jiang
paper_authors._id: 67fc9e72b2383c63dc413dcf
paper_authors.name: Ran Chen
paper_authors._id: 67fc9e72b2383c63dc413dd0
paper_authors.name: Jian Guo
paper_title: SQL-R1: Training Natural Language to SQL Reasoning Model By
    Reinforcement Learning
paper_summary: Natural Language to SQL (NL2SQL) enables intuitive interactions with
  databases by transforming natural language queries into structured SQL
  statements. Despite recent advancements in enhancing human-computer interaction
  within database applications, significant challenges persist, particularly
  regarding the inference performance in complex scenarios involving multi-table
  joins and nested queries. Current methodologies primarily utilize supervised
  fine-tuning (SFT) to train the NL2SQL model, which may limit adaptability and
  interpretability in new environments (e.g., finance and healthcare). In order
  to enhance the reasoning performance of the NL2SQL model in the above complex
  situations, we introduce SQL-R1, a novel NL2SQL reasoning model trained by the
  reinforcement learning (RL) algorithms. We design a specialized RL-based reward
  function tailored for NL2SQL tasks and discussed the impact of cold start on
  the effectiveness of intensive training. In addition, we achieve competitive
  accuracy using only a tiny amount of synthetic NL2SQL data for augmented
  training and further explore data engineering for RL. In existing experiments,
  SQL-R1 achieves execution accuracy of 88.6% and 66.6% on the benchmark Spider
  and BIRD, respectively, only using the 7B base model.
paper_ai_summary: A novel NL2SQL model named SQL-R1 leverages reinforcement learning to improve reasoning in complex scenarios, achieving high accuracy with minimal data.
paper_ai_keywords: supervised fine-tuning reinforcement learning rewards function cold start synthetic data data engineering