Some training environments exploit an asymmetry in which a complex data-generating process is easy to construct or run backward from, while a model must spend substantial work inferring the hidden process from its outputs.

“there's environments where it's easier to go backwards and forwards and like what i mean by that is like it's very easy to define this like complex data generating process and this is like this kind of latent variable you keep hidden from the model um you can generate like arbitrarily complex like environments and the model has to do a lot of like irreducible like token spend and irreducible work to figure out what that data generation process was um there's asymmetries in terms of like you know you can inject information from the real world so like anthropic finds a bug you”