MBZUAI / MediX-R1-2B

MediX-R1: Open-Ended Medical Reinforcement Learning

Sahal Shaji Mullappilly *, Mohammed Irfan K *, Omair Mohamed , Mohamed Zidan , Fahad Khan , Salman Khan , Rao Muhammad Anwer , and Hisham Cholakkal

* Equally contributing first authors

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

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Overview

MediX-R1 is an open-ended Reinforcement Learning (RL) framework for medical multimodal large language models (MLLMs) that enables clinically grounded, free-form answers beyond multiple-choice formats. MediX-R1 fine-tunes vision-language backbones with Group-Based RL and a composite reward tailored for medical reasoning: an LLM-based accuracy reward, a medical embedding-based semantic reward, and lightweight format and modality rewards that enforce interpretable reasoning.

Despite using only ~50K instruction examples, MediX-R1 achieves excellent results across standard medical LLM and VLM benchmarks, outperforming strong open-source baselines.

Highlights:

• Our 8B model achieves an overall average of 68.8% , outperforming the much larger 27B MedGemma (68.4%).
• Our 30B model achieves the best overall score of 73.6% , demonstrating the effectiveness of our composite reward design.

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Contributions

• We introduce an open-ended RL framework for medical MLLMs that produces clinically grounded, free-form answers beyond MCQ formats.
• We design a composite reward combining LLM-based accuracy, embedding-based semantic similarity, format adherence, and modality recognition, providing stable and informative feedback where traditional verifiable or MCQ-only rewards fall short.
• We propose a unified evaluation framework for both text-only and image+text tasks using a Reference-based LLM-as-judge, capturing semantic correctness, reasoning, and contextual alignment.
• Despite using only ~50K instruction examples, MediX-R1 achieves state-of-the-art results across diverse medical LLM and VLM benchmarks, with particularly large gains on open-ended clinical tasks.

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Architecture

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Composite Reward Design

MediX-R1 uses a multi-signal reward combining LLM-based accuracy, embedding-based semantic similarity, format adherence, and modality recognition. This stabilizes training and prevents reward hacking compared to single-signal approaches.

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Qualitative Examples

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Training

We provide training configs for all model sizes using GRPO and DAPO algorithms. The training pipeline uses a vLLM-based reward server for LLM-as-judge scoring during RL training.

cd training
pip install -e .
bash vllm_serve.sh       # Step 1: Start the reward server
bash run_train.sh        # Step 2: Launch RL training
bash merge_model.sh      # Step 3: Merge FSDP checkpoints

Training data: MBZUAI/medix-rl-data (~51K train, ~2.5K test samples)

See training/README.md for detailed setup, configuration options, and per-model scripts.

Evaluation

We propose a unified evaluation framework for both text-only (LLM) and image+text (VLM) tasks using a Reference-based LLM-as-judge across 17 medical benchmarks.

cd eval
pip install uv && uv pip install -r requirements.txt
bash eval.sh             # Run all phases: generate, evaluate, score

Supports self-hosted judge models via vLLM or OpenRouter as a remote alternative. Results can be submitted to the MediX Leaderboard .

See eval/README.md for task selection, CLI reference, and MMMU-Medical evaluation.

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Model Zoo

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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}, 
}

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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.

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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.