Infrastructure for RL in trillion-parameter MoE models
A briefing on infrastructure techniques for scaling reinforcement-learning workloads on large MoE models, including FP8 and parallelism.
The briefing offers a deep dive into training trillion-parameter mixture-of-experts (MoE) models using prime-rl 0.6.0. Topics include FP8, wide expert parallelism, prefill/decode disaggregation (P/D disaggregation), router replay, and 3-D parallelism. It presents techniques for scaling reinforcement-learning workloads without providing quantitative results.
These topics are relevant to teams studying large-scale training and inference, but do not replace evaluating your own hardware and performance requirements. If you use AI to study or apply the material, avoid sending personal data or internal information unnecessarily, and follow your organization’s data policy.