Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL

This post details the implementation of asynchronous GRPO (Proximal Policy Optimization) with LoRA (Low-Rank Adaptation) for training Mixture of Experts (MoE) models across Hugging Face Jobs. It highlights a novel approach that bypasses traditional NCCL for distributed communication, instead utilizing a bucket and proxy mechanism. This enables efficient distributed training of MoE models without requiring direct GPU-to-GPU communication via NCCL, potentially reducing infrastructure complexity and cost.