Model merging broadens the scope of machine learning.
Model merging in machine learning is gaining traction as a crucial technique that allows for the enhancement of models without direct access to raw data or substantial computational resources. However, observing a gap in literature, this survey contributes significantly by examining the current methods, theories, and practical applications of model merging, alongside prospective areas for research. Furthermore, it introduces a novel taxonomic structure while emphasizing both the challenges and prospects that lie ahead in the domain of model merging.
Read more: [
Research and Development
](https://arxiv.org/abs/2408.07666)