@inproceedings{mullappilly-etal-2026-medix,
title = "{M}edi{X}-{R}1: Open Ended Medical Reinforcement Learning",
author = "Mullappilly, Sahal Shaji and
Kurpath, Mohammed Irfan and
Kaithakkodan, Jaseel Muhammad and
Mohamed, Omair and
Zidan, Mohamed and
Khan, Fahad Shahbaz and
Khan, Salman and
Anwer, Rao Muhammad and
Cholakkal, Hisham",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2133/",
pages = "45507--45529",
ISBN = "979-8-89176-462-0",
abstract = "In this work, we introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical vision language models (VLMs) that enables 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 evaluate performance, we employ a unified evaluation framework for both text-only and multimodal (image+text) tasks that uses a Reference-based LLM-as-judge in place of brittle string-overlap metrics, capturing semantic correctness, clinical accuracy, and contextual alignment. Our results show that open-ended RL with comprehensive reward signals can effectively strengthen medical reasoning in multimodal models. Despite using only {\textasciitilde}51K instruction examples, MediX-R1 achieves strong performance across standard medical LLM (text-only) and VLM (image + text) benchmarks, outperforming existing open-weight medical VLMs, with large gains particularly on open-ended clinical tasks. These improvements remain consistent across multiple RL algorithms and VLM backbones and are further supported by blinded evaluations with medical experts. Our trained models, curated datasets and source code are available at https://medix.cvmbzuai.com"
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%0 Conference Proceedings
%T MediX-R1: Open Ended Medical Reinforcement Learning
%A Mullappilly, Sahal Shaji
%A Kurpath, Mohammed Irfan
%A Kaithakkodan, Jaseel Muhammad
%A Mohamed, Omair
%A Zidan, Mohamed
%A Khan, Fahad Shahbaz
%A Khan, Salman
%A Anwer, Rao Muhammad
%A Cholakkal, Hisham
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F mullappilly-etal-2026-medix
%X In this work, we introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical vision language models (VLMs) that enables 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 evaluate performance, we employ a unified evaluation framework for both text-only and multimodal (image+text) tasks that uses a Reference-based LLM-as-judge in place of brittle string-overlap metrics, capturing semantic correctness, clinical accuracy, and contextual alignment. Our results show that open-ended RL with comprehensive reward signals can effectively strengthen medical reasoning in multimodal models. Despite using only ~51K instruction examples, MediX-R1 achieves strong performance across standard medical LLM (text-only) and VLM (image + text) benchmarks, outperforming existing open-weight medical VLMs, with large gains particularly on open-ended clinical tasks. These improvements remain consistent across multiple RL algorithms and VLM backbones and are further supported by blinded evaluations with medical experts. Our trained models, curated datasets and source code are available at https://medix.cvmbzuai.com
%U https://aclanthology.org/2026.findings-emnlp.2133/
%P 45507-45529
Markdown (Informal)
[MediX-R1: Open Ended Medical Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.2133/) (Mullappilly et al., Findings 2026)
ACL
- Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Omair Mohamed, Mohamed Zidan, Fahad Shahbaz Khan, Salman Khan, Rao Muhammad Anwer, and Hisham Cholakkal. 2026. MediX-R1: Open Ended Medical Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 45507–45529, Budapest, Hungary. Association for Computational Linguistics.