A. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.

B. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.

C. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.

D. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.