# Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL

arXiv: https://arxiv.org/abs/2505.20315 · HTML: https://mlsanity.com/paper/2505.20315/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 44 of its 57 references that are in the map.
- First posted on arXiv 22 May 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 30 of 44)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement](https://mlsanity.com/paper/2409.12122/) (2024)
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://mlsanity.com/paper/2501.09686/) (2025)
- [Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning](https://mlsanity.com/paper/2502.14768/) (2025)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://mlsanity.com/paper/2502.21321/) (2025)
- [OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models](https://mlsanity.com/paper/2411.04905/) (2024)
- [Competitive Programming with Large Reasoning Models](https://mlsanity.com/paper/2502.06807/) (2025)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale](https://mlsanity.com/paper/2503.02240/) (2025)
- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't](https://mlsanity.com/paper/2503.16219/) (2025)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023)

## Built on by (most cited first, 30 of 38)

- [Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling](https://mlsanity.com/paper/2509.24403/) (2025)
- [MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL](https://mlsanity.com/paper/2511.01008/) (2025)
- [AgentSM: Semantic Memory for Agentic Text-to-SQL](https://mlsanity.com/paper/2601.15709/) (2026)
- [Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL](https://mlsanity.com/paper/2604.17073/) (2026)
- [SLM-SQL: An Exploration of Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2507.22478/) (2025)
- [MTIR-SQL: Multi-turn Tool-Integrated Reasoning Reinforcement Learning for Text-to-SQL](https://mlsanity.com/paper/2510.25510/) (2025)
- [Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation](https://mlsanity.com/paper/2511.21402/) (2025)
- [SING-SQL: A Synthetic Data Generation Framework for In-Domain Text-to-SQL Translation](https://mlsanity.com/paper/2509.25672/) (2025)
- [RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System](https://mlsanity.com/paper/2508.17590/) (2025)
- [Agent Bain vs. Agent McKinsey: A New Text-to-SQL Benchmark for the Business Domain](https://mlsanity.com/paper/2510.07309/) (2025)
- [Learning to Self-Evolve](https://mlsanity.com/paper/2603.18620/) (2026)
- [SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation](https://mlsanity.com/paper/2603.16161/) (2026)
- [TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas](https://mlsanity.com/paper/2603.16448/) (2026)
- [PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning](https://mlsanity.com/paper/2509.07159/) (2025)
- [AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesis](https://mlsanity.com/paper/2512.23366/) (2025)
- [Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL](https://mlsanity.com/paper/2512.17053/) (2025)
- [EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2601.04875/) (2026)
- [Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL](https://mlsanity.com/paper/2602.15564/) (2026)
- [Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering](https://mlsanity.com/paper/2603.20004/) (2026)
- [PExA: Parallel Exploration Agent for Complex Text-to-SQL](https://mlsanity.com/paper/2604.22934/) (2026)
- [R3-SQL: Ranking Reward and Resampling for Text-to-SQL](https://mlsanity.com/paper/2604.25325/) (2026)
- [Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL](https://mlsanity.com/paper/2605.04719/) (2026)
- [SQLConductor: Search-to-Policy Learning for Step-wise Text-to-SQL Orchestration](https://mlsanity.com/paper/2606.23537/) (2026)
- [EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL](https://mlsanity.com/paper/2606.23693/) (2026)
- [SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation](https://mlsanity.com/paper/2609.08950/) (2026)
- [RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning](https://mlsanity.com/paper/2601.05451/) (2026)
- [IESR:Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models](https://mlsanity.com/paper/2602.05385/) (2026)
- [Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding](https://mlsanity.com/paper/2604.28028/) (2026)
- [FINER-SQL: Boosting Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2605.03465/) (2026)
- [SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback](https://mlsanity.com/paper/2606.01246/) (2026)

## Abstract (arXiv)

Translating natural language into SQL (Test2SQL) is a longstanding challenge at the intersection of natural language understanding and structured data access. While large language models (LLMs) have significantly improved fluency in SQL generation, producing correct and executable SQL--particularly for complex queries--remains a bottleneck. We present Arctic-Text2SQL-R1, a reinforcement learning (RL) framework and model family designed to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness. Our approach avoids brittle intermediate supervision and complex reward shaping, promoting stable training and alignment with the end task. Combined with carefully curated data, strong supervised initialization, and effective training practices, Arctic-Text2SQL-R1 achieves state-of-the-art execution accuracy across six diverse Test2SQL benchmarks, including the top position on the BIRD leaderboard. Notably, our 7B model outperforms prior 70B-class systems, highlighting the framework's scalability and efficiency. We further demonstrate inference-time robustness through simple extensions like value retrieval and majority voting. Extensive experiments and ablation studies offer both positive and negative insights, providing practical guidance for future Test2SQL research.