Review Pretraining

Pretraining is the broad first stage of training where a model learns general patterns from a very large dataset before any narrower tuning.

Pretraining is the broad initial training stage that teaches a model general patterns from a large dataset before the model is adapted to a narrower task or use.

Pretraining shapes the capabilities and limitations that later prompting or fine-tuning builds on. Its data, objective, and scale influence every downstream use.

A language model first learns to predict text across a large corpus, then receives supervised fine-tuning for instruction following.

Pretraining does not make a model ready for every use. It creates a base that may still need adaptation, evaluation, safeguards, and application-specific context.

Learn broad patterns first; specialize later.

* [[training|Training]] * [[foundation-model|Foundation Model]] * [[fine-tuning|Fine-Tuning]] * [[training-data|Training Data]]

* [Jacob Devlin and colleagues, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”](https://arxiv.org/abs/1810.04805) — demonstrates broad language-model pretraining followed by task-specific fine-tuning.

* Compare [[training|Training]], [[foundation-model|Foundation Model]], [[fine-tuning|Fine-Tuning]], and [[training-data|Training Data]] to place Pretraining in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.