I think Rich Sutton's bitter lesson will prove to apply here, and what we really need to advance machine learning capabilities are more general and powerful models capable of learning for themselves - better able to extract and use knowledge from the firehose of data available from the real world (ultimately via some form of closed-loop deployment where they can act and incrementally learn from their own actions).
What OpenAI have delivered here is basically a hack - a neuro-symbolic agent that has a bunch of hard-coded "reasoning" biases built in (via RL). It's a band-aid approach to try to provide some of what's missing from the underlying model which was never designed for what it's now being asked to do.
What OpenAI have delivered here is basically a hack - a neuro-symbolic agent that has a bunch of hard-coded "reasoning" biases built in (via RL). It's a band-aid approach to try to provide some of what's missing from the underlying model which was never designed for what it's now being asked to do.