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MARS-SQL
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# MARS-SQL: A Multi-Agent Reinforcement Learning Framework for Text-to-SQL [](https://arxiv.org/abs/2511.01008) [](https://icml.cc/virtual/2026/poster/65053) [](https://opensource.org/licenses/MIT) [](https://www.python.org/) This repository contains the official implementation of MARS-SQL, accepted at the **43rd International Conference on Machine Learning (ICML 2026)**. ## 🧭 Overview  ## 📁 Repository layout | Path | Contents | | :--- | :--- | | [`Mars-train/`](./Mars-train/) | Reinforcement-learning environment, configuration, and training entry point | | [`Mars-inference/`](./Mars-inference/) | Generation, candidate selection, and execution-based evaluation tools | | [`data/`](./data/) | Prepared Parquet inputs for BIRD, Spider, and validation runs | | [`figs/`](./figs/) | Architecture and workflow figures used by the documentation | ## 📚 Citation Please cite the ICML 2026 paper using the entry below (also available as [`CITATION.bib`](./CITATION.bib)): ```bibtex @inproceedings{yang2026marssql, title={A Multi-Agent Reinforcement Learning Framework For Text-To-SQL}, author={Yang, Haolin and Zhang, Youran and others}, booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)}, year={2026}, url={https://icml.cc/virtual/2026/poster/65053} } ``` --- ## 🚀 Implementation ### 1. Training #### Environment Setup Please refer to the [training guide](./Mars-train/README.md) and [installation notes](./Mars-train/Install.md) for environment setup using `uv` and Ray. #### Dataset Preparation 1. Download the **BIRD dataset** (dev/train databases) from the [official BIRD benchmark page](https://bird-bench.github.io/). 2. Unzip the dataset and note the absolute path to the database directory. #### ⚙️ Configuration The entry point reads local paths and credentials from environment variables; source files do not need to be edited. At minimum, set `DB_PATH`, `CKPT_PATH`, and `WANDB_API_KEY`. #### Run Training Once configured, execute the training script: ```bash export DB_PATH=/absolute/path/to/bird/databases export CKPT_PATH=/absolute/path/to/checkpoints export WANDB_API_KEY=your_wandb_api_key bash Mars-train/mars-train.sh ``` ### 2. Inference We recommend running inference in a separate environment to avoid dependency conflicts. **Environment Setup** ```bash # (Optional, but recommended) Create and activate a new virtual environment conda create -n mars-infer python=3.10 -y conda activate mars-infer # Install all required packages cd MARS-SQL/Mars-inference pip install -r requirements.txt ``` **💾 Using Pre-trained Models** Our trained MARS-SQL models (based on Qwen-7B) are publicly available on Hugging Face: | Model Name | Description | Hugging Face Link | | :--- | :--- | :--- | | **Qwen-SQL-7B-bird\_5turns\_80step** | Trained with 5 turns | [Yanghl0526/Qwen-SQL-7B-bird\_5turns\_80step](https://huggingface.co/Yanghl0526/Qwen-SQL-7B-bird_5turns_80step) | | **Qwen-SQL-7B-bird\_10turn** | Trained with 10 turns | [Yanghl0526/Qwen-SQL-7B-bird\_10turn](https://huggingface.co/Yanghl0526/Qwen-SQL-7B-bird_10turn) | **Run Inference** The following command will generate 16 trajectories for each question in the dataset: ```bash export DB_PATH=/absolute/path/to/bird/databases bash inference.sh ``` The output will be saved as `step80_bird_@16_turn5_test_result.parquet` ### 📊 Evaluation After generating the inference results (parquet file), use the evaluation script to calculate metrics. ```bash python evaluate_sql.py --input_file step80_bird_@16_turn5_test_result.parquet --db_path Bird_DB_PATH ``` See the [inference guide](./Mars-inference/README.md) for all configurable paths, model overrides, and candidate-selection utilities.
