ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
By Yang Yang · Paper · cs.CV
Existing scaling strategies for Multimodal Large Language Models (MLLMs) typically expand either model parameters or sequential inference computation, incurring substantial memory or latency overhead. More importantly, most existing methods fail to alter the rigid, fixed computat