(Meta: was this meant to be a question?)
In contrast, the generalization-focused approach puts less emphasis on the assumption that the worst catastrophes are intentional.
I don't think this is actually a con of the generalization-focused approach. From the post you link, one of the two questions in that approach (the one focused on robustness) is:
How do we ensure the model generalizes acceptably out of distribution?
Part of the problem is to come up with a good definition of "acceptable", such that this is actually possible to achieve. (See e.g. the "Defining acceptable" section of this post, or the beginning of this post.) But if you prefer to bake in the notion of intent, you could make the second question
How do we ensure the model continues to try to help us when out of distribution?
Great, I feel pretty resolved about this conversation now.
I think Evan's Clarifying Inner Alignment Terminology is quite clever; more well-optimized than it may at first appear. However, do think there are a couple of things which don't work as well as they could:
My current proposal would be the following:
This is a complex and tricky issue, and I'm eager to get thoughts on it.
Relevant reading:
As a reminder, here are Evan's definitions. Nested children are subgoals; it's supposed to be the case that if you can achieve all the children, you can achieve the parent.
So we split impact alignment into intent alignment and capability; we split intent alignment into outer alignment and objective robustness; and, we achieve objective robustness through inner alignment.
Here's what my proposed modifications do:
"Objective Robustness" disappears from this, because inner+outer gives intent-alignment directly now. This is a bit of a shame, as I think objective robustness is an important subgoal. But I think the idea of objective robustness fits better with the generalization-focused approach:
And it's fine for there to be multiple different subgoal hierarchies, since there may be multiple paths forward.