The speaker gives an example in which Anthropic found a bug using tens of thousands of humans and language models, then converted it into an environment that a single language model could theoretically discover within a few million tokens.

“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”

“bug um through like you know tens of thousands of human and llms combined and like turn that into a very very neat environment which in a single lm could theoretically find within like you know a few million tokens so like there's all these asymmetries which we're cherry picking and like we're counting on like kind of this task horizon generalization um but i think yeah again it's just going to hit diminishing returns at some point like at some point it's diminishing returns and how hard it is to create those environments in the first place like”