Comment by imtringued
8 hours ago
>My broader point is just that there's nothing inherent to the structure of LLMs that stops them from updating their weights and continuously learning from environmental feedback in the way humans do, and there's already solid templates for how they could push even further in that direction.
"LLM" is a branded model as a product. Of course it could be anything, as long as it fulfills the product category.
But we live in reality, we can only look at what models are out there and we see that they don't do any of those things and yet we're supposed to act as if these models already do.
Ok just say "transformer" then.
What can a transformer not do that people say they can do?
The parent comment said, paraphrasing, "learn from interaction with the world", and I'm responding, they absolutely can already do this by taking their logs of interaction with humans and updating their weights through backprop.
The reason you don't see that done "live" is primarily an economics problem rather than a limitation of the model structure.
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.