What is Continual Pre-Training? 

Continual pre-training is a training process that’s used, most often, for deep knowledge injection into large language models (LLMs) – with the goal of continually enhancing the model's performance. Initially, the base or foundation model is trained on a large, general dataset to learn language patterns, grammar, and context. This foundational stage allows the model to develop a broad understanding of language.

Continual pre-training enables the model to stay updated with the latest information and to adapt to specific tasks – such as understanding specialized industry terms or evolving contexts – without forgetting what it has already learned. This technique ensures that the model remains both versatile and accurate across a wide range of applications over time.