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Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

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Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

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). Among these stages, large-scale RL training places unusual demands on infrastructure because it combines elastic inference work with tightly coupled model training that requires high-bandwidth communication. Reward models, verifiers, and checkpoint updates add further memory, networking, and orchestration pressure. This type of multi-workload optimization reflects a common infrastructure challenge when you run model training, inference, and evaluation on shared resources without creating bottlenecks or leaving capacity idle.

Compared with dense models, post-training MoE models introduce a new infrastructure challenge: as newer MoE architectures become increasingly sparse to reduce inference costs, training becomes constrained more by communication than by compute. A key source of this communication overhead is Expert Parallelism (EP). EP introduces dynamic all-to-all token routing across devices, in addition to the dense, structured communication patterns of Tensor Parallelism (TP), Data Parallelism (DP), and Pipeline Parallelism (PP).


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