Micah Goldblum

NYU fits a pretraining–RL scaling law on chess, with RL's optimal share rising to 28%

A team from NYU, Modal Labs, UCLA, UIUC and Columbia trained 10 chess models from 5M to 1B parameters to fit a joint pretraining-RL scaling law, with the compute-optimal RL share rising from about 19-20% at 50-80M parameters to 28% at 680M.