When you post-train a Mixture-of-Experts (MoE) model with Reinforcement Learning from Human Feedback (RLHF) or Group Relative Policy Optimization (GRPO) at scale, three simultaneous challenges emerge.

The first requires coordinating heterogeneous compute for rollout generation and policy training.

Second, sustaining high-throughput communication across hundreds of accelerators.

And third, dynamically orchestrating every subsystem to keep them in balance.

On AWS, you can address these challenges using Amazon Elastic Kubernetes Service (Amazon EKS), Elastic Fabric Adapter (EFA), and DeepEP.

Mixture-of-Experts (MoE) has become a standard architecture for scaling large language models (LLMs) to hundreds of billions or even trillions of parameters, while maintaining efficient inference through sparsity.

However, sparsity doesn’t remove infrastructure complexity in training.

As part of the standard training pipeline, these models must undergo pre-training, mid-training, supervised fine-tuning (SFT), and reinforcement learning (RL).

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