I think the presentation of the points is confusing people, the model could understand weights directly for extremely small networks in the 10s of thousands of weights.
Once they moved onto even the smallest LLMs, what it did was closer to abliteration: create examples where the model does something you don't want, create examples where the model it doesn't do the thing, then most importantly do forward passes.
Once you're allowed to actually do a forward pass vs just reading raw weights, tracking the changes in activations and tweak the model to modify them is a very well established process that takes a few hundred lines of Python.
I think the presentation of the points is confusing people, the model could understand weights directly for extremely small networks in the 10s of thousands of weights.
Once they moved onto even the smallest LLMs, what it did was closer to abliteration: create examples where the model does something you don't want, create examples where the model it doesn't do the thing, then most importantly do forward passes.
Once you're allowed to actually do a forward pass vs just reading raw weights, tracking the changes in activations and tweak the model to modify them is a very well established process that takes a few hundred lines of Python.