Continual learning is most successful when the differences between tasks are small. When new tasks are similar to those previously learned, the updates required to adapt the model are less likely to interfere with existing knowledge. This scenario is often described as small task shifts, where the underlying data distributions or objectives of the tasks do not differ drastically. In such cases, parameter updates made for the new task do not significantly disrupt the parameters responsible for performance on previous tasks, reducing the risk of catastrophic forgetting.

Another favorable condition for continual learning is the presence of shared representations. When multiple tasks rely on overlapping features or structures within the data, the model can develop internal representations that serve more than one task. This overlap allows the model to generalize across tasks and maintain performance, as the features learned for one task remain beneficial for others. Shared representations act as a stabilizing factor, since changes made for one task are less likely to harm performance on tasks with similar requirements.

Overparameterization also plays a significant role in enhancing continual learning outcomes. Larger models, with more parameters than strictly necessary for a single task, can allocate different subsets of their capacity to different tasks. This flexibility enables the model to learn new tasks by adjusting unused or less critical parameters, thus reducing interference with parameters that are important for previously learned tasks. Overparameterized models are therefore better equipped to handle sequential learning without catastrophic forgetting, as they can compartmentalize knowledge more effectively.

Starting from pretrained models further improves continual learning, especially when the pretrained representations are robust and general. Pretraining on large, diverse datasets allows the model to acquire features that are broadly useful across many tasks. When new tasks are introduced, the model can adapt these robust representations with minimal changes, making it less likely to overwrite important parameters associated with earlier tasks. This initialization strategy leverages prior knowledge and provides a strong foundation for continual learning.

Key takeaways: continual learning is most effective when tasks are related, representations are shared, and models are sufficiently large.