In plain words
Training is the process that turns data and feedback into a model’s learned parameters. It repeatedly measures how the model performs and updates adjustable values to improve a chosen objective.
A closer look
In a typical neural-network training loop, the model processes a batch of examples, a loss function measures error, backpropagation computes gradients, and an optimizer updates parameters. This is repeated across many batches. The objective may involve predicting labels, predicting tokens, or improving a reward signal.
Pretraining often builds broad capabilities from large datasets. Later training can specialize behavior or improve instruction following. Data quality, coverage, compute, and the objective all affect the result. Evaluation on held-out cases checks whether improvement extends beyond the examples used to update the model.
In practice
A language model sees part of a sentence and predicts the next token. Its parameters are adjusted so that the observed continuation receives a more appropriate probability across many such examples.
A useful distinction
Training and inference are separate concepts. Supplying context or asking a model to revise an answer usually runs the existing model again; it does not necessarily update its parameters or make it permanently learn from the exchange.