I mean, in what sense has a Decision Transformer like Gato not already learned to do it by extensive 1-step prediction?
I don’t think Gato does the sort of training-in-simulation that Dreamer does. And that training-in-simulation seems like a major part of intelligence. So I think Dreamer has a component needed[1] for AGI that Gato lacks.
As we know perfectly well by now, frozen weights do not preclude runtime learning, and Gato is trained on meta-learning tasks (MetaWorld and Procgen, plus the real-world datasets which are longtailed and elicit meta-learning in GPT-3 etc). They also mention adding Transformer-XL recurrent memory at runtime.
Gato supports a sequence length of only 1048, which means that it cannot remember its meta-”learned” things for very long. Non-frozen weights would eliminate that problem.
Well, “needed”, you could perhaps brute-force your way to a solution to AGI without this component, but then the problem is that Gato does not have enough dakka to be generally intelligent.
I don’t think Gato does the sort of training-in-simulation that Dreamer does. And that training-in-simulation seems like a major part of intelligence. So I think Dreamer has a component needed[1] for AGI that Gato lacks.
Gato supports a sequence length of only 1048, which means that it cannot remember its meta-”learned” things for very long. Non-frozen weights would eliminate that problem.
Well, “needed”, you could perhaps brute-force your way to a solution to AGI without this component, but then the problem is that Gato does not have enough dakka to be generally intelligent.