Another generator-discriminator gap: telling whether an outcome is good (outcome->R) is much easier than coming up with plans to achieve good outcomes. Telling whether a plan is good (plan->R) is much harder, because you need a world model (plan->outcome) as well, but for very difficult tasks it still seems easier than just coming up with good plans off the bat. However, it feels like the world model is the hardest part here, not just because of embeddedness problems, but in general because knowing the consequences of your actions is really really hard. So it seems like for most consequentialist optimizers, the quality of the world model actually becomes the main thing that matters.
This also suggests another dimension along which to classify our optimizers: the degree to which they care about consequences in the future (I want to say myopia but that term is already way too overloaded). This is relevant because the further in the future you care about, the more robust your world model has to be, as errors accumulate the more steps you roll the model out (or the more abstraction you do along the time axis). Very low confidence but maybe this suggests that mesaoptimizers probably won’t care about things very far in the future because building a robust world model is hard and so perform worse on the training distribution, so SGD pushes for more myopic mesaobjectives? Though note, this kind of myopia is not quite the kind we need for models to avoid caring about the real world/coordinating with itself.
Another generator-discriminator gap: telling whether an outcome is good (outcome->R) is much easier than coming up with plans to achieve good outcomes. Telling whether a plan is good (plan->R) is much harder, because you need a world model (plan->outcome) as well, but for very difficult tasks it still seems easier than just coming up with good plans off the bat. However, it feels like the world model is the hardest part here, not just because of embeddedness problems, but in general because knowing the consequences of your actions is really really hard. So it seems like for most consequentialist optimizers, the quality of the world model actually becomes the main thing that matters.
This also suggests another dimension along which to classify our optimizers: the degree to which they care about consequences in the future (I want to say myopia but that term is already way too overloaded). This is relevant because the further in the future you care about, the more robust your world model has to be, as errors accumulate the more steps you roll the model out (or the more abstraction you do along the time axis). Very low confidence but maybe this suggests that mesaoptimizers probably won’t care about things very far in the future because building a robust world model is hard and so perform worse on the training distribution, so SGD pushes for more myopic mesaobjectives? Though note, this kind of myopia is not quite the kind we need for models to avoid caring about the real world/coordinating with itself.