How does contrastive learning scale in distributed training systems?

It scales using distributed batch processing and cross-device negative sampling.

In large-scale contrastive learning, batch size directly impacts performance due to the number of negative samples. Distributed training systems synchronize embeddings across GPUs using all-gather operations. Techniques like memory banks (MoCo) or queue-based negatives allow scaling without enormous batch sizes. Communication efficiency is critical in multi-node systems.