From a scientific viewpoint, MOE models are experiments in dividing and conquering large AI tasks. Sharing experts across layers is one way to reuse learned knowledge and reduce the overall model size. This can be helpful when computational power is limited. However, letting each layer have its own experts lets the model specialize more deeply at each step. Researchers often test both approaches to find which works best for a given problem.