I disagree -- I think if you can nail down better what's happening and why and get a thorough handling on the mechanics, its limitations, its costs, etc you open the door to a) major efficiency wins b) improvements in rigor of said reasoning?

Right now we're playing a stochastic game with the weights, and getting major incremental improvements. But if we have a more formal modeling of how reasoning happens in them (whether we can even call it, that) we can potentially apply optimizations, adaptations of existing symbolic AI techniques, etc. to substantially shrink/optimize the models or make the inference process more efficient and more reliable.

Partially agree: yes we should endeavor to learn as much as possible about how these reasoning strategies work. It will pay dividends in enhancing and aligning the models.

But the stochastic game IS the win. That is exactly why they are able to find solutions is seemingly infinite solution spaces. Your symbolic techniques can only get you gains in narrow domains and by the time you figure out how to make it work for your niche domain, the next all-purpose LLM release will crush your results with stochastic games. (OK maybe over-exaggerating a bit here but these stochastic games over the language space is why we can pull together knowledge from many domains.)