Google DeepMind has released a new paper on distributed training titled 'Streaming DiLoCo with Overlapping Communication.' This research introduces methods for training data-parallel models globally while minimizing bandwidth usage, achieving a reduction of 400 times in the amount of data exchanged. Key innovations include streaming partial updates, which allow synchronization of only a subset of gradients, and overlapping communication with computation, enabling synchronization during the computation of inner steps. The approach aims to lower barriers for communities looking to build their own models, making distributed training more accessible.