Survey of 16 open-source async RL libraries
huggingface surveyed 16 open-source rl libraries solving the gpu idle time problem. synchronous rl training keeps gpus idle 60% of the time because data generation takes hours. the solution is disaggregating inference and training using rollout buffers and async weight transfers. ray dominates orchestration, nccl is the default for weight sync, and distributed moe support is emerging as the key differentiator.
Hugging Face surveyed 16 open-source reinforcement learning libraries that implement asynchronous architectures to address GPU inefficiencies in post-training. The analysis identifies that synchronous RL training often leaves training GPUs idle 60% of the time due to long inference rollouts, particularly with reasoning models and agent workflows. The study categorizes solutions across seven axes, revealing that Ray dominates orchestration primitives, NCCL is the standard for weight synchronization, and support for distributed Mixture-of-Experts (MoE) is becoming a critical differentiator as model scale increases.