This blog post introduces the 'Masters' framework for distilling knowledge from large vision-language models to smaller models, addressing challenges in stability and performance through a novel approach using masking and reinforcement learning. It aims to enhance the efficiency of deploying VLMs on mobile and edge devices by improving knowledge transfer and representation learning.