How do large-scale unsupervised systems perform distributed representation alignment?

They align embeddings across distributed workers using synchronization, contrastive sharing, and global memory structures.

In distributed training, each worker produces embeddings from local data shards. To ensure global consistency, systems use all-gather operations, shared memory banks, or parameter servers. Contrastive objectives require cross-device negatives, which forces embedding synchronization. Advanced systems also use momentum encoders to stabilize alignment across asynchronous updates.