title: Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards
    for Reasoning-Enhanced Text-to-SQL
summary: Text-to-SQL is a challenging task involving multiple reasoning-intensive
  subtasks, including natural language understanding, database schema
  comprehension, and precise SQL query formulation. Existing approaches often
  rely on handcrafted reasoning paths with inductive biases that can limit their
  overall effectiveness. Motivated by the recent success of reasoning-enhanced
  models such as DeepSeek R1 and OpenAI o1, which effectively leverage
  reward-driven self-exploration to enhance reasoning capabilities and
  generalization, we propose a novel set of partial rewards tailored specifically
  for the Text-to-SQL task. Our reward set includes schema-linking, AI feedback,
  n-gram similarity, and syntax check, explicitly designed to address the reward
  sparsity issue prevalent in reinforcement learning (RL). Leveraging group
  relative policy optimization (GRPO), our approach explicitly encourages large
  language models (LLMs) to develop intrinsic reasoning skills necessary for
  accurate SQL query generation. With models of different sizes, we demonstrate
  that RL-only training with our proposed rewards consistently achieves higher
  accuracy and superior generalization compared to supervised fine-tuning (SFT).
  Remarkably, our RL-trained 14B-parameter model significantly outperforms larger
  proprietary models, e.g. o3-mini by 4% and Gemini-1.5-Pro-002 by 3% on the BIRD
  benchmark. These highlight the efficacy of our proposed RL-training framework
  with partial rewards for enhancing both accuracy and reasoning capabilities in
  Text-to-SQL tasks.
paper_authors._id: 67ed3cd4ec3d31f7c7591f56
paper_authors.name: Mohammadreza Pourreza
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paper_authors.name: Shayan Talaei
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paper_authors.fullname: Shayan Talaei
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paper_authors.name: Ruoxi Sun
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paper_authors.fullname: Ruoxi Sun
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paper_authors._id: 67ed3cd4ec3d31f7c7591f59
paper_authors.name: Xingchen Wan
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paper_authors.fullname: X Wan
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paper_authors.user: masonxw
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paper_authors.name: Hailong Li
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paper_authors.name: Azalia Mirhoseini
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paper_authors.fullname: Azalia Mirhoseini
paper_authors.type: user
paper_authors.user: am34
paper_authors._id: 67ed3cd4ec3d31f7c7591f5c
paper_authors.name: Amin Saberi
paper_authors._id: 67ed3cd4ec3d31f7c7591f5d
paper_authors.name: Sercan "O. Arik
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paper_authors.fullname: Sercan Arik
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paper_title: Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards
    for Reasoning-Enhanced Text-to-SQL
paper_summary: Text-to-SQL is a challenging task involving multiple reasoning-intensive
  subtasks, including natural language understanding, database schema
  comprehension, and precise SQL query formulation. Existing approaches often
  rely on handcrafted reasoning paths with inductive biases that can limit their
  overall effectiveness. Motivated by the recent success of reasoning-enhanced
  models such as DeepSeek R1 and OpenAI o1, which effectively leverage
  reward-driven self-exploration to enhance reasoning capabilities and
  generalization, we propose a novel set of partial rewards tailored specifically
  for the Text-to-SQL task. Our reward set includes schema-linking, AI feedback,
  n-gram similarity, and syntax check, explicitly designed to address the reward
  sparsity issue prevalent in reinforcement learning (RL). Leveraging group
  relative policy optimization (GRPO), our approach explicitly encourages large
  language models (LLMs) to develop intrinsic reasoning skills necessary for
  accurate SQL query generation. With models of different sizes, we demonstrate
  that RL-only training with our proposed rewards consistently achieves higher
  accuracy and superior generalization compared to supervised fine-tuning (SFT).
  Remarkably, our RL-trained 14B-parameter model significantly outperforms larger
  proprietary models, e.g. o3-mini by 4% and Gemini-1.5-Pro-002 by 3% on the BIRD
  benchmark. These highlight the efficacy of our proposed RL-training framework
  with partial rewards for enhancing both accuracy and reasoning capabilities in
  Text-to-SQL tasks.
paper_ai_summary: A novel set of partial rewards for reinforcement learning improves the accuracy and generalization of large language models in the Text-to-SQL task compared to supervised fine-tuning.
paper_ai_keywords: Text-to-SQL natural language understanding database schema comprehension SQL query formulation reward-driven self-exploration reinforcement learning reward sparsity group relative policy optimization large language models intrinsic reasoning skills BIRD benchmark