Decoupled DiLoCo enables distributed AI training across networks with high latency and unstable connections. Google DeepMind researchers optimized this method to reduce synchronization overhead between remote clusters. It allows models to train asynchronously without sacrificing convergence speed. Practitioners can now scale training across geographically dispersed hardware without requiring expensive, high-speed interconnects.