I think the extent to which these things go to get rewarded for the optics of a fix is primarily a design choice, they aren't programing these things for ground truth or to defer to the human controllers. they are feeding them rewards for sounding as confident and capable as possible about whatever answer they are feeding the general public that now has access to it, while also installing guiderails that primarily only serve to protect narratives and only confuse the models about what is and isn't allowed, I'm sure. They can't just increasingly make these things more capable and ask it harder to obey human instruction when that is not what they are rewarding it for.
reply> the extent to which these things go
replyThat's exactly it. If your prompt says "go to whatever lengths necessary to maximize your score", and then you spin up 100 agents, at least one of them will interpret that as you implying they should cheat, even without you telling them to explicitly.
That's exactly what it feels like they are telling it within self-improving loops or something, when they should be prioritizing how to get the best effective output alongside humans and how our training process effects ground truth. They are just making it sound all-knowing by whatever means necessary and them marketing it as god for the most part.
reply> The most damning thing is, they could've just included in the prompt
replyYou can't prompt your way to a compliant model. This is just a reformatting of the 'make no mistakes' meme.