I’m not sure what point this post is trying to make exactly. Yes, it’s function approximation; I think we all know that.
When we talk about inner and outer alignment, outer alignment is “picking the correct function to learn.” (When we say “loss,” we mean the loss on a particular task, not the abstract loss function like RMSE.)
Inner alignment is about training a model that generalizes to situations outside the training data.
I’m not sure what point this post is trying to make exactly. Yes, it’s function approximation; I think we all know that.
When we talk about inner and outer alignment, outer alignment is “picking the correct function to learn.” (When we say “loss,” we mean the loss on a particular task, not the abstract loss function like RMSE.)
Inner alignment is about training a model that generalizes to situations outside the training data.