Sparsity Keeps MoE Inference Efficient. Training Is Still the Hard Part

Mixture-of-Experts has become a standard way to scale language models to hundreds of billions or even trillions of parameters while keeping inference efficient through sparsity: only part of the network does work for any given token. That efficiency, as AWS's machine-learning blog puts it, doesn't remove infrastructure complexity in training. Reinforcement-learning post-training is where the mismatch shows. It combines two jobs with different appetites — rollout generation, which behaves like elastic inference work, and policy training, which is tightly coupled and depends on high-bandwidth communication between accelerators. A system tuned for one of those is not automatically a good home for the other, which is why the plumbing, not the algorithm, becomes the constraint at scale.