Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications, and Opportunities

Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive computation. As model merging becomes increasingly prevalent across various fields, it is crucial to understand the available model merging techniques comprehensively. However, there is a significant gap in the literature regarding a systematic and thorough review of these techniques. This survey provides a comprehensive overview of model merging methods and theories, their applications in various domains and settings, and future research directions. Specifically, we first propose a new taxonomic approach that exhaustively discusses…

• NN
  National Natural Science Foundation of China

  Award: 62576364, 62576083, 62432003, U25A20431, 62411540034
• CP
  China Postdoctoral Science Foundation

  Award: 2025M781535