In machine learning, a training step that trains a general‐purpose model (sometimes called a foundation model) on publicly‐available data. Pre‐training is often followed by fine‐tuning to equip the model with task‐specific information.
Sources:
NIST SP 800-226

  A component of the training stage in which a model learns general patterns, features, and relationships from vast amounts of unlabeled data, such as through self-supervised learning. Pre-training can equip models with knowledge of gen­eral features or patterns which may be useful in downstream tasks, and can be followed with additional training or fine-tuning that specializes the model for a specific downstream task.
Sources:
NIST AI 100-2e2025 under pre-training