MedBridgeRL | Kimia Shaban

Medical vision-language models (VLMs) are increasingly trained with reinforcement learning (RL) on top of supervised fine-tuning (SFT). But when does RL actually help — and how much of the apparent gain is due to the vision encoder, the SFT stage, or RL itself?

This project systematically disentangles these contributions across a range of medical imaging benchmarks, providing clearer guidance for practitioners on when RL is worth the extra complexity.

Links: Paper (arXiv) · Project Page

Status: Preprint (2026)

(others & Shaban, 2026)

References

2026

Preprint
When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains

others and Kimia Shaban

arXiv preprint, 2026

Abs arXiv Bib HTML Website

We study when reinforcement learning provides meaningful gains over supervised fine-tuning for medical vision-language models, disentangling the contributions of vision encoders, SFT, and RL to model performance.
@article{shaban2026medbridgerl, title = {When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains}, author = {others and Shaban, Kimia}, year = {2026}, journal = {arXiv preprint}, }