What is the term for the training method where model weight updates occur simultaneously?

The training method where model weight updates occur simultaneously is called Synchronous Updates. In this approach, multiple processes or nodes work together to update the model's weights at the same time after aggregating their gradients. This method ensures that all training instances contribute to the weight update in a coordinated manner, promoting consistency across the model's learning.

Synchronous updates are particularly beneficial in distributed training scenarios where multiple GPUs or machines are utilized, as they can help achieve faster convergence and improve the overall efficiency of the training process.

The other options represent different concepts. Asynchronous updates refer to a training strategy where model weight updates happen independently, leading to potential inconsistency in the model state. Gradient checkpointing is a memory optimization technique used to save GPU memory during training by selectively storing certain activations. The objective function is a mathematical representation that the model aims to optimize during training.