An iteration or step is one update of model weights after processing one batch, the atomic unit of training. Definition: Forward pass on batch, compute loss, backward pass, optimizer step = one iteration. Relationship to epochs: steps_per_epoch = dataset_size / batch_size. Total steps = epochs x steps_per_epoch. LLM training: Often measured in steps rather than epochs. Millions of steps for large models. What happens each step: Load batch, forward pass, compute loss, backward pass (gradients), optimizer update, (optional logging). With gradient accumulation: Logical step may span multiple forward-backward passes before optimizer update. Logging frequency: Log every N steps (e.g., 100). Too frequent is expensive, too infrequent misses issues. Checkpointing: Save model every N steps or epochs. Balance between safety and storage. Learning rate per step: Most schedulers update LR per step, not per epoch. Smoother adaptation. Steps vs samples: Sometimes report samples (steps x batch size) for comparisons across batch sizes. Progress tracking: Steps are wall-clock-neutral metric. Epochs depend on dataset size.