Robot Learning

Epoch

Epoch is one complete pass of a training algorithm over the entire training dataset. Training typically spans many epochs with the data reshuffled each pass, and validation metrics tracked per epoch guide learning-rate schedules, checkpoint selection, and early stopping. For very large or streaming datasets, training is often measured in gradient steps or tokens instead, with less than one nominal epoch completed.

Why it matters for physical AI

On small demonstration datasets policies can overfit within tens of epochs while action accuracy keeps improving, so epoch-level checkpointing paired with real rollout evaluation, not just validation loss, guides model selection.