A combo of LM + REPL would be the winning ticket. The language model generates a piece of code, executes it, observe results. Is the answer found? No, maybe it cannot be solved by one single iteration, continue until solution found or max number of iterations reached. Single step responses are limited by their nature, and computation is hard to do "in your head"/"in your LM".

The REPL could also come with libraries for all the important algorithms. Thus the LM could use numerical solvers and other nice toys.

Another excellent module we could augment LM's with is a search engine - already done, proven to improve the efficiency of the LM by 25x (see link). Another one would be a physics simulator.

https://deepmind.com/research/publications/2021/improving-la...

We're just starting with language models. They will improve tremendously.

But a really really intelligent AI would try to run them through a neural net and be amazed when it got most of them approximately correct.

I'd really want to see in a super complicated AI, given many questions, devising a way to compile a basic calculator program that obeys the simple math rules (e.g only addition and multiplication) for itself inline and calculate the thrown questions using that instead of training math data.