parameterization

This paper develops a principled scaling theory for Mixture-of-Experts (MoE) architectures, introducing the Maximally Scale-Stable Parameterization (MSSP) that ensures stable training and hyperparameter transfer across width, depth, expert width, and number of experts, validated by experiments.