Optimizing Model Merging and Decentralized Training

The chapter explores the inefficiencies of model merging compared to specialized models for diverse texts and proposes ensembling specialized models during inference time instead. It introduces parameter expansion as a solution for catastrophic forgetting in models and advocates for a decentralized model framework to enhance performance across domains. The discussion emphasizes the importance of sharing models in a structured way, enabling user control over data coverage, and a novel approach to training language models conscientiously.