Datasets:

MBZUAI
/

medix-rl-data

dataset_info:
  features:
    - name: id
      dtype: string
    - name: image
      list:
        image:
          mode: RGBA
    - name: source
      dtype: string
    - name: problem
      dtype: string
    - name: solution
      dtype: string
  splits:
    - name: test
      num_bytes: 1226261011
      num_examples: 2451
    - name: train
      num_bytes: 22901597169
      num_examples: 51335
  download_size: 47058109860
  dataset_size: 24127858180
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
      - split: train
        path: data/train-*
license: cc
task_categories:
  - image-text-to-text
language:
  - en
tags:
  - medical
  - biology
size_categories:
  - 10K<n<100K

MediX-R1: Open-Ended Medical Reinforcement Learning

MediX-R1

Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE

Model Zoo

Model	HuggingFace
MediX-R1-2B	MBZUAI/MediX-R1-2B
MediX-R1-8B	MBZUAI/MediX-R1-8B
MediX-R1-30B	MBZUAI/MediX-R1-30B

Citation

If you use MediX-R1 in your research, please cite our work as follows:

@misc{mullappilly2026medixr1openendedmedical,
      title={MediX-R1: Open Ended Medical Reinforcement Learning}, 
      author={Sahal Shaji Mullappilly and Mohammed Irfan Kurpath and Omair Mohamed and Mohamed Zidan and Fahad Khan and Salman Khan and Rao Anwer and Hisham Cholakkal},
      year={2026},
      eprint={2602.23363},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.23363}, 
}

License

This project is released for research purposes only under CC-BY-NC-SA 4.0 License. It is not intended for clinical or commercial use.

Users are urged to employ MediX-R1 responsibly, especially when applying its outputs in real-world medical scenarios. It is imperative to verify the model's advice with qualified healthcare professionals and not rely on it for medical diagnoses or treatment decisions.

Acknowledgements

We are thankful to EasyR1 (a fork of veRL) for their open-source RL training framework.

This work was partially supported with NVIDIA Academic Grant 2025 and MBZUAI-IITD Research Collaboration Seed Grant.

We are grateful to MBZUAI for compute and support.