Pretraining teaches a model broad language patterns by asking it to predict missing or next tokens across a very large dataset. This stage builds most of the model's general knowledge and basic abilities. The result is often called a base model.
Post-training teaches that base model how to behave for particular uses. It can include examples of good instruction following, human or AI preference feedback, safety training, tool use, and reinforcement learning on tasks with verifiable answers.
The distinction matters because a knowledgeable base model may only continue text instead of answering a request. Post-training makes it more helpful and easier to control, but it can also introduce biases such as excessive agreement or refusal. Both stages shape the final behavior.
Rewriting in plainer words…
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