Prestraining

Prestraining is a training paradigm in machine learning that refers to a preparatory phase of training applied to a model before the main training stage. The aim is to shape representations or initialize parameters in a way that facilitates subsequent learning, often by exposing the model to broad, diverse, or related tasks on unlabeled or weakly labeled data. The term is not universally standardized and is sometimes used interchangeably with pretraining or described as a specific form of curriculum-inspired preparation.

Typical methods in prestraining include self-supervised objectives such as masked reconstruction or contrastive learning, auxiliary proxy tasks like predicting rotations or domain labels, and the use of synthetic or augmented data. Data sources may consist of large, multi-domain corpora or domain-relevant synthetic datasets. The expected outcome is a model with richer representations that can be fine-tuned more effectively on downstream tasks or that generalizes better to out-of-domain data, low-resource tasks, or zero-shot settings.

Benefits of prestraining include improved sample efficiency, faster convergence, and enhanced generalization, particularly when downstream data are limited or diverse. It can also reduce sensitivity to overfitting in early training stages. However, it adds computational cost and design complexity, since poorly aligned prestraining objectives can lead to negative transfer or wasted resources. Selecting appropriate proxy tasks and data sources is a key consideration.

Prestraining is used across domains such as natural language processing, computer vision, and audio, often in transfer learning and continual learning contexts. See also pretraining, curriculum learning, self-supervised learning, and transfer learning.