MediX-R1: Open Ended Medical Reinforcement Learning
We introduce MediX-R1, 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 a baseline vision-language backbone with Group Based RL and a composite reward tailored for medical reasoning: an LLM-based accuracy reward that judges semantic correctness with a strict YES/NO decision, a medical embedding-based semantic reward to capture paraphrases and terminology variants, and lightweight format and modality rewards that enforce interpretable reasoning and modality recognition. This multi-signal design provides stable, informative feedback for open-ended outputs where traditional verifiable or MCQ-only rewards fall short. To measure progress, we propose a unified evaluation framework for both text-only and image+text tasks that uses a Reference-based LLM-as-judge in place of brittle string-overlap metrics, capturing semantic correctness, reasoning, and contextual alignment. Despite using only sim51K instruction examples, MediX-R1 achieves excellent results across standard medical LLM (text-only) and VLM (image + text) benchmarks, outperforming strong open-source baselines and delivering particularly large gains on open-ended clinical tasks. Our results demonstrate that open-ended RL with comprehensive reward signals and LLM-based evaluation is a practical path toward reliable medical reasoning in multimodal models. Our trained models, curated datasets and source code are available at https://medix.cvmbzuai.com
69a12e58a13deaa4494489fc
Sahal Shaji Mullappilly
claimed_verified
2026-02-27T16:39:40.482Z
62e23c7f555a866437a53cd0
https://cdn-avatars.huggingface.co/v1/production/uploads/62e23c7f555a866437a53cd0/UaAsYZQXuwb4NSG5WnvdG.jpeg
Sahal Shaji
sahalshajim
user
sahalshajim
69a12e58a13deaa4494489fd
Mohammed Irfan Kurpath
claimed_verified
2026-02-27T16:39:38.185Z
650289dbc130d99814b34dc5
/avatars/ff0cf5add144cd79c41a255f41f34efb.svg
K Mohammed Irfan
k-m-irfan
user
k-m-irfan
69a12e58a13deaa4494489fe
Omair Mohamed
69a12e58a13deaa4494489ff
Mohamed Zidan
69a12e58a13deaa449448a00
Fahad Khan
69a12e58a13deaa449448a01
Salman Khan
69a12e58a13deaa449448a02
Rao Anwer
69a12e58a13deaa449448a03
Hisham Cholakkal
MediX-R1: Open Ended Medical Reinforcement Learning
We introduce MediX-R1, 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 a baseline vision-language backbone with Group Based RL and a composite reward tailored for medical reasoning: an LLM-based accuracy reward that judges semantic correctness with a strict YES/NO decision, a medical embedding-based semantic reward to capture paraphrases and terminology variants, and lightweight format and modality rewards that enforce interpretable reasoning and modality recognition. This multi-signal design provides stable, informative feedback for open-ended outputs where traditional verifiable or MCQ-only rewards fall short. To measure progress, we propose a unified evaluation framework for both text-only and image+text tasks that uses a Reference-based LLM-as-judge in place of brittle string-overlap metrics, capturing semantic correctness, reasoning, and contextual alignment. Despite using only sim51K instruction examples, MediX-R1 achieves excellent results across standard medical LLM (text-only) and VLM (image + text) benchmarks, outperforming strong open-source baselines and delivering particularly large gains on open-ended clinical tasks. Our results demonstrate that open-ended RL with comprehensive reward signals and LLM-based evaluation is a practical path toward reliable medical reasoning in multimodal models. Our trained models, curated datasets and source code are available at https://medix.cvmbzuai.com
MediX-R1 presents an open-ended reinforcement learning framework for medical multimodal large language models that uses diverse reward signals and LLM-based evaluation to improve clinical reasoning beyond multiple-choice formats.
Reinforcement Learning vision-language backbone Group Based RL LLM-based accuracy reward medical embedding-based semantic reward lightweight format reward lightweight modality reward Reference-based LLM-as-judge medical reasoning multimodal large language models