Model Training (Training)

First, Understand What It Does

Model training uses data and objectives to repeatedly adjust model parameters, making it perform better on a class of tasks.

Understanding with an Example

Using a language model as an example, you give it "Before class, please open your textbook" and let it predict the next token based on preceding content. Initially the predictions are inaccurate; the training program calculates the error and adjusts the parameters. It learns the patterns from large numbers of samples, not just memorizing this particular sentence.

Going a Step Further

Each training step typically uses a batch of data. The model first performs a forward computation, the loss function produces a differentiable number, backpropagation computes the gradients, the optimizer updates the parameters, and this repeats with the next batch. Here "training" involves explicit parameter changes; when you add context in a chat box, you're usually only modifying the current input.

What It Doesn't Tell You

Training objectives, data quality, and evaluation methods together determine the results. Getting the loss very low doesn't mean all facts, reasoning, and aesthetics are reliable. Regular users don't need to train a model first to clearly describe their task.

Next Steps

• Pretraining
• Post-training
• Loss Function
• Optimizer

References

• PyTorch · Optimizing Model Parameters: Batches, learning rates, loss, gradients, SGD updates, and test set evaluation.
• Hugging Face · How do Transformers work?: Pretraining vs. fine-tuning, different architectures, and training objectives.

Information verified on 2026-09-09; original papers are used to explain mechanisms, and examples in the text are for instructional purposes only.