A new transformer variant has been created to facilitate more efficient model training in distributed settings. 128x compression with no significant loss in convergence rates, increases in memory, or compute overhead

Macrocosmos has released a paper on ResBM (Residual Bottleneck Models), a new transformer-based architecture designed for low-bandwidth pipeline-parallel training. https://arxiv.org/abs/2604.11947 ResBM introduces a residual encoder-decoder bottleneck across pipeline boundaries, with the goal of red