Mixture of Expert

Mixture-of-Experts (MoE) models aim to improve the efficiency and scalability of large language and other models by using multiple specialized "expert" networks, each handling a subset of the input data. Current research focuses on improving routing algorithms to efficiently assign inputs to experts, developing heterogeneous MoE architectures with experts of varying sizes and capabilities, and optimizing training methods to address challenges like load imbalance and gradient conflicts. This approach holds significant promise for creating larger, more powerful models with reduced computational costs, impacting various fields from natural language processing and computer vision to robotics and scientific discovery.