Fundamentally the current language-model approach is lacking in any general reasoning ability, so they are trying to mitigate this by using synthetic data and reinforcement learning to bake specific reasoning chains into the model, one domain at a time ... coding, math, hacking, three.js competence ...

The trouble with this is that there is little generalization in the utility of these baked-in reasoning chains from one domain to the next, so in the end this is not dissimilar to the CYC project's decades long attempt to encode all of human knowledge into a giant expert system... the hope is that if you make your collection of jagged narrow intelligences sufficiently large then it will look more like general intelligence, not a bed of nails.

I would assume that the gains from this type of test-time compute (and synthetic RLVR dataset) scaling will level out just the same as gains from human training set scaling eventually levelled out, and basically for the same reason - because you are tapping into a finite data pool, whether language itself, or reasoning steps isolated from that language, so at some point the incremental gains become increasingly small (10->20% is a doubling, 90->95% is just a ~5% gain).

It's not clear where all the different AI companies are currently focusing - on some of these narrow verticals, or on growing the forest of narrow intelligences. OpenAI's chief scientist, Jakub Pachocki, said that their current focus is on RSI(!) - improving the model in ways that will help them iterate faster in order to have a "fire meets fire" tool than can combat enemy AIs. It's not clear what this really means - what skill set makes an LLM more helpful in the process of building LLMs, but it seems to basically be process automation.