Scaling laws project that a model with more parameters trained for longer on more data yields predictably better performance, and that generally you want to sca

Scaling laws project that a model with more parameters trained for longer on more data yields predictably better performance, and that generally you want to scale these factors commensurately. More of the compute budget is being spent on RLVR [0] for which we also fit scaling laws Researchers tweak data mix, reward shape, model architecture, etc etc, breakthroughs which reduce the cost to train a just-as-smart model. But this increases the returns to scale, which further incentivizes bigger models trained for longer on more data [0] "...to run reinforcement learning training...at pretraining scale." https://x.ai/news/grok-4?_bhlid=b9339d7816a05adeb52bae7050cc74d4fdc85352 https://x.ai/news/grok-4?_bhlid=b9339d7816a05adeb52bae7050cc...