Continual Pretraining

Continual Pretraining is a continuous learning method that involves constantly updating model parameters to adapt to new data and tasks, aiming to improve the model's generalization ability and real-time adaptability. This approach has significant advantages in dynamic environments, effectively enhancing model performance, reducing reliance on large-scale labeled data, and increasing the model's practicality and flexibility.