Pre-train

Pre-train refers to the initial training phase where AI models learn foundational representations from large, unlabeled datasets before being adapted for specific tasks through fine-tuning. This unsupervised or self-supervised learning process enables models to acquire general knowledge, patterns, and features that transfer across diverse applications. Pre-training typically employs objectives like next-token prediction for language models, masked language modeling for BERT-style architectures, or contrastive learning for vision models. The process requires massive computational resources and datasets containing billions of examples, creating versatile base models with broad capabilities. Popular pre-training approaches include autoregressive generation, denoising autoencoders, and multi-modal learning across text, images, and audio. Pre-trained models serve as starting points for subsequent fine-tuning, enabling faster convergence and better performance on downstream tasks. For AI agents, pre-training provides the foundational intelligence necessary for reasoning, language understanding, and multi-domain knowledge essential for autonomous decision-making.