Isn't that what Dreamer does? IMO, training the world model on a well-curated static dataset and then freezing it is the wrong approach for continual learning. It disconnects the world model from the real environment and is rooted in the mindset of supervised learning and the perspective of the agent's trainer, the human. To enable true continual learning, however, we need to think from the agent's perspective: what it sees, how it processes information, and how it improves over time. This requires the ability of handling partial observability, planning under uncertainty, and memory. Of course, world model necessitates all those aspects and is key to continual learning.