MediX-R1

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

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.

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.

Architecture

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.

Qualitative Examples

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.

Model Zoo

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 c