[Xelynega]

For well-known games like "Pokemon Red" I wonder how much of that game knowledge would be "smuggled in" by an LLM in it's training data if you just replaced the external info in the reward function with it/used it to make up for other deficiencies.

I think they allude to this in their conclusion, but it's less about the low-hanging fruit and more about designing a system to feedback game dialogue into the RL decision making process in a way that can be mutated as part of the RL(be it an LLM or something else)