There must be. Since there is a finite amount of real world reward signal captured by these models, there can only be a finite amount of grounded knowledge that

There must be. Since there is a finite amount of real world reward signal captured by these models, there can only be a finite amount of grounded knowledge that can come out. Usually without reasoning, the output knowledge "yield" will be far lower than input. The interesting piece is if we can get LLMs to reason well, that finite reward capture can take LLMs much farther than possibly humans could do with the same signal.