New research papers propose methods to optimize visual token pruning in VLMs

Recent papers introduce techniques like RUTA, DIVE, and GSTEP to reduce the computational cost of processing long visual token sequences in vision-language models. These methods aim to improve inference efficiency for images and videos by optimizing how redundant tokens are identified and pruned.