Large-scale RL is different from either large-scale pretraining or large-scale inference. A key difference is that the training weights and the sampler weights should ideally match as closely as possible, which requires the trainers sending weight updates to the samplers. This creates a bottleneck: one must either use high speed intra-cluster networking to send weight updates or accept the often-meaningful delay that comes with attempting to communicate terabytes of model weights, not to mention blocking on the samplers. We introduce a faster weight update paradigm, in most cases allowing better-than-intra-cluster speeds, regardless of whether the samplers and trainers are co-located within the same cluster.