So it's not able to insert new knowledge, but it's able to adjust the routing/looping/weighing of existing one.
As I understand, it can replace post-training like LoRA/GRPO, but if it'd cheaper the authors don't go into
If I want to do that in a generalizable way that preserves performance across all domains then am I not just back to training again?
I think you could still essentially have a really cheap Lora-Post training on a frozen base model by doing this. And instead of creating 200 checkpoints for different post-trained directions just adjust a single one on the fly.
There doesn't need to be specific narrow training data on every conceivable task.
It's not directly related to this but I'd like to add that, in my opinion (admittedly uninformed but it makes sense to me), when they manage to, not just reduce the frequency of the LLM stating made up dates, laws etc, or made up facts, but manage to make an AI understand what the truth is and deliberately make responses accurate, we'll have real AGI that will be able to do engineering, accounting, medicine... unsupervised.
I doubt it's that simple, but I could totally see an LLM being able to make that association and apply a fix from an improved LLM to a smaller one with the same architecture.
I mean, this question indicates that your understanding of how these models work is, indeed, lacking.
Despite what some people having being hollering about, treating LLMs as just "stochastic parrots" isn't doing their capabilities any justice. They are constantly exhibiting emergent behavior which wouldn't be found directly in their training data. They are capable of combining different ideas that they are explicitly taught to perform novel tasks.
I'm not saying you're guilty of this, but the people claiming LLMs just regurgitate existing data don't understand how LLMs, and likely their own cognitive experience, actually works.
This hand-wavy phrase has applied to neural networks in general since it was possible to train them. That was more than half a century ago. It means both nothing and everything simultaneously.
Every learned ability of any neural network is an emergent property.