Recent advances in finetuning multimodal large language models

Abstract

Finetuning serves as the critical adaptation mechanism for multimodal large language models, bridging their pretrained knowledge with specialized downstream task requirements. This paper reviews recent finetuning advances across three key dimensions: (1) efficiency-oriented methods that reduce resource costs; (2) capability-specific techniques enhancing specialized multimodal skills; and (3) task-unifying approaches that bridge understanding and generation. We demonstrate how these directions transform multimodal large language models from versatile foundations into adaptive, human-aligned systems, providing researchers with a structured roadmap for developing next-generation multimodal AI.