Apple scales categorical flow maps to a 1.7B-parameter model

Apple researchers trained a 1.7B-parameter base flow model on 2.1T tokens, distilling it into a Categorical Flow Map that generates diverse text in as few as 4 inference steps with near-data-level token entropy. They also introduced a likelihood bound for CFMs in the semi-discrete setting, scoring in the same range as discrete diffusion on LM benchmarks.