Building upon the foundational concepts of sparse Mixture of Experts models introduced earlier, this chapter examines more sophisticated architectural variations. The effectiveness of an MoE model often hinges on the design of its components, particularly the gating network responsible for routing tokens to experts.

We will analyze techniques for designing effective gating networks, including $top-k$ routing and the use of noise for exploration during training. You will learn about hierarchical MoE structures which allow for finer-grained specialization by arranging experts in multiple layers. We will compare different router architectures, such as linear, non-linear, and attention-based mechanisms, evaluating their trade-offs.

Further discussions will cover practical considerations like determining appropriate expert capacity and sizing, along with methods to improve the stability and learning dynamics of the gating networks themselves. The chapter concludes with a practical exercise focused on implementing custom gating mechanisms in code.