Having established the foundational concepts and architectural variations of Mixture of Experts models, this chapter focuses on the practical aspects of training them effectively. Training sparse MoE models presents unique challenges compared to their dense counterparts, primarily related to ensuring balanced computation across experts and stable learning of the routing mechanism.

We will examine the critical issue of load balancing, where uneven distribution of inputs to experts can lead to inefficiency and hinder model performance. You will learn about auxiliary loss functions, often added to the main task loss as $L_{total} = L_{task} + \alpha L_{aux}$, designed specifically to encourage more uniform expert utilization during training. We will cover common formulations for $L_{aux}$ and techniques for tuning the balancing coefficient $\alpha$.

Further topics include strategies for optimizing the router or gating network itself, ensuring it learns meaningful specializations without collapsing. We will address practical considerations such as handling tokens that exceed expert capacity ("dropped tokens") and diagnosing scenarios where experts fail to differentiate their functions. Finally, we will look at how standard choices like optimizers and learning rate schedules interact with MoE training stability. The chapter concludes with practical implementation exercises focusing on load balancing techniques.