Alright let's assume your premise is true, that transformers can learn from interaction with the world by updating their weights - then why isn't this done?

Because backprop fundamentally wants the entire data set in every pass. It doesn't behave well and is destructive when you update after the pre-training phase. RLHF/LORA are attempts to work around that and effective at what they do, but it is not learning in the sense you are talking about and also do not fully address the catastrophic forgetting problem. This architecture as is - is not compatible with continual learning.