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The first and most expensive phase of training a large model, where the network learns general representations from a massive unlabeled corpus. For language models the objective is usually next-token prediction over trillions of tokens of internet text, books, and code. Pretraining produces a base model that knows a lot but follows no instructions.
Frontier-lab roles spend most of their compute budget on pretraining. Even engineers who never train at this scale need to understand what pretraining buys and what it does not.
The first and most expensive phase of training a large model, where the network learns general representations from a massive unlabeled corpus. For language models the objective is usually next-token prediction over trillions of tokens of internet text, books, and code. Pretraining produces a base model that knows a lot but follows no instructions.
Frontier-lab roles spend most of their compute budget on pretraining. Even engineers who never train at this scale need to understand what pretraining buys and what it does not.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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