What is a benefit of asynchronous updates during training?

The benefit of asynchronous updates during training is primarily the reduction of communication overhead. In traditional synchronous training, all participants must share their updates at the same time, which can lead to delays and increased communication costs, especially when using distributed systems. Asynchronous updates allow different components of the model to train independently and share their updates without waiting for others, which significantly reduces the time spent on communication. This efficiency gain enables faster convergence of the training process and improves resource utilization.

In contrast, the other options focus on aspects that may not directly relate to the asynchronous mechanism. Increased network bandwidth refers to the available capacity of the network, which is a separate issue from the update method itself. Minimizing computation time might be a goal of training optimization strategies, but it's not a direct outcome of using asynchronous updates. Enhanced model accuracy is often a result of improved training dynamics or data quality rather than the training process's timing structures.