SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning 事件

SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning arXiv:2602.01990v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually expand their capabilities, making Multimodal Continual Instruction Tuning (MCIT) essential. Recent methods leverage sparse expert routing to promote task specialization, but we find that the expert routing proc