  Large Vision-Language Models (LVLMs) have shown significant potential in
assisting medical diagnosis by leveraging extensive biomedical datasets.
However, the advancement of medical image understanding and reasoning
critically depends on building high-quality visual instruction data, which is
costly and labor-intensive to obtain, particularly in the medical domain. To
mitigate this data-starving issue, we introduce Self-Training Large Language
and Vision Assistant for Medical (STLLaVA-Med). The proposed method is designed
to train a policy model (an LVLM) capable of auto-generating medical visual
instruction data to improve data efficiency, guided through Direct Preference
Optimization (DPO). Specifically, a more powerful and larger LVLM (e.g.,
GPT-4o) is involved as a biomedical expert to oversee the DPO fine-tuning
process on the auto-generated data, encouraging the policy model to align
efficiently with human preferences. We validate the efficacy and data
efficiency of STLLaVA-Med across three major medical Visual Question Answering
(VQA) benchmarks, demonstrating competitive zero-shot performance with the
utilization of only 9% of the medical data.
