The speaker says reinforcement-learning-trained language models have substantial base knowledge and can perform things that were not possible before.

“problem in reinforcement learning that you know when it found move 37 it was kind of innovative but not creative it was creative to us because we understood the creative space we understood how it all hung together and now with these RL trained language models they don't understand it quite in the way that we do but they solve the exploration problem by understanding how things fit together so they can just explore novel trajectories they have a lot of base knowledge they clearly do things”

“that weren't possible before but the grounding thing is interesting so it's not necessarily an argument that you need to have physical instantiation you need to have consciousness but there is a spectrum of representations down this abstraction mountain and the models seem to be actually using the representations at multiple levels of resolution but it's not like intelligence is this magical quality I still think that it has you know it relates to a scope of tasks and it relates”