Batch Processing in LLM Training

A neural network starts out with weights that produce poor predictions. Training is the process of nudging those weights, again and again, until the predictions improve. No single nudge is enough, and the network never sees the whole dataset as one lump. Instead, the training code is a set of nested repetitions: an outer loop that goes over the entire training set, and an inner loop that works through that set in smaller chunks.

Understanding the PyTorch training workflow means understanding what happens inside those loops and why each step exists. The approach taken here starts from the smallest unit of learning, one weight update, and then builds outward. Once you can explain what a single update needs, the outer repetitions and the monitoring steps become simple additions rather than a memorized list.

Every weight update in a PyTorch training run follows the same core cycle, made of four operations. Each one has a specific job, and they must happen in this order for each batch.

The four operations form one round. When the loop moves to the next batch, the cycle begins again from the reset step. The loop therefore repeats the same four-step round over and over, and each round is one weight update.

Rather than computing one update from the entire training set at once, the training loop processes the data in batches. Each batch is a group of training examples, and the loop iterates over the batches inside every epoch. Each pass through a batch produces one round of the core update cycle, so the number of batches determines how many weight updates happen during one complete pass through the data.

The batch count depends on two quantities: how many training examples exist and how many examples each batch holds. A larger batch size means fewer batches per pass, and a smaller batch size means more batches. Because the batch count comes from this division, you can work it out before training begins.

Training usually runs for more than one epoch. The outer loop repeats the whole pass, and the inner loop repeats the batch cycle inside it. The result is a nested structure: the epoch loop sets the number of full passes, and within each pass the batch loop performs the core update cycle once per batch. Iterating over epochs is the first step of the workflow for this reason, because it defines the outermost repetition that everything else lives inside.

The workflow ends with two steps that are not needed for learning itself. The first prints the training and validation set losses so you can watch progress. The second generates sample text so you can inspect what the model currently produces. The source describes these as optional steps used for tracking training progress. They change nothing about the weights; they only let the person running training see how things are going.