This is just supposed to be an (admittedly informal) restatement of the definition of outer alignment in the context of an objective function where the data distribution plays a central role.
For example, assuming a reinforcement learning objective function, outer alignment is equivalent to the statement that there is an aligned policy that gets higher average reward on the training distribution than any unaligned policy.
I did not intend to diminish the importance of robustness by focusing on outer alignment in this post.
This is just supposed to be an (admittedly informal) restatement of the definition of outer alignment in the context of an objective function where the data distribution plays a central role.
For example, assuming a reinforcement learning objective function, outer alignment is equivalent to the statement that there is an aligned policy that gets higher average reward on the training distribution than any unaligned policy.
I did not intend to diminish the importance of robustness by focusing on outer alignment in this post.