Earlier quoted context omitted.
Which part do you find objectionable - the lack of progress in previous years or the current/future potential of transformers in RL? I do work in ML but mostly applications instead of research.
One big issue is how exactly we'll continue to scale. Exponential growth is hard to maintain. An example: In the Chinchilla paper [1], the authors suggest that most big transformer models are undertrained, and that we will probably see diminishing returns in scaling up the size of networks if we don't also scale up the size of the datasets. They have a subanalysis where they extrapolate out how big datasets will need…
In the RL space, a sufficiently complex, stochastic environment is effectively a data generator.