I think my big disagreement is with point one—yes, if you fix the architecture as something with bad alignment properties, then there is probably some dataset / reward signal that still gives you a good outcome. But this doesn’t work in real life, and it’s not something I see people working on such that there needs to be a word for it.
What deserves a word is people starting by thinking about both what we want the AI to learn and how, and picking datasets and architectures in tandem based on a theoretical story of how the AI is going to learn what we want it to.
A number of reasonable outer alignment proposals such as iterated amplification, recursive reward modeling and debate use generic objectives such as reinforcement learning (and indeed, none of them would work in practice without sufficiently high data quality), so it seems strange to me to dismiss these objectives.
I think my big disagreement is with point one—yes, if you fix the architecture as something with bad alignment properties, then there is probably some dataset / reward signal that still gives you a good outcome. But this doesn’t work in real life, and it’s not something I see people working on such that there needs to be a word for it.
What deserves a word is people starting by thinking about both what we want the AI to learn and how, and picking datasets and architectures in tandem based on a theoretical story of how the AI is going to learn what we want it to.
A number of reasonable outer alignment proposals such as iterated amplification, recursive reward modeling and debate use generic objectives such as reinforcement learning (and indeed, none of them would work in practice without sufficiently high data quality), so it seems strange to me to dismiss these objectives.