Self-Improvement in Multimodal Large Language Models: A Survey

Shijian Deng, Kai Wang, Tianyu Yang, Harsh Singh, Yapeng Tian

Self-Improvement in Multimodal Large Language Models: A Survey: 21 upvotes on Hugging Face Daily Papers, #8 of 36 papers on 2025-10-06. Day-by-day upvote history.

Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions.

Paper page on Hugging Face · arXiv

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