I do personally, but there is some disagreement about this in the field. In fact, I would go further and say that (in addition to using large pre-trained models) we will need methods of training that are pretty substantially different in order to elicit robust reasoning behavior.
Even supposing I'm wrong about this, if you go and look at the scaling plots in figure 3 and try to figure out how big your model would need to be in order to be solving most of these problems, you'd get a really big number. Even if you had such a big model, it would still require post-processing of the samples to actually get the right answers. From the perspective of applications, that's fine, but it's a little unsatisfying from the perspective of studying intelligence. Even with those caveats (!) these problems aren't that hard compared to general software engineering tasks...
> What do you think about leveraging unsupervised training to improve program synthesis? Could synthesized programs be executed on generated input in a way that supports contrastive learning [5]?
I think this is an interesting idea and someone should try it! I do think that, even restricting our attention to just getting neural networks to execute programs, that we will need to do something a little more drastic to robustly get the results we want.