John Martabano Landy

Meta's ads ranking and retrieval fleet no longer allocates capacity by hand, one model at a time. Previously each scaling decision cost days to weeks of training, and the architecture dimensions are coupled, so they cannot be enlarged independently. Axia maps embedding dimension, interaction depth and interaction width through a scaling layer into complete architectures, then uses Bayesian surrogates to pick a small number of training trials. Across 34 model iterations in two development cycles, offline normalized entropy improved by a mean 0.66% relative and the online key metric by a cumulative 0.36%.

Meta's ads ranking and retrieval fleet no longer allocates capacity by hand, one model at a time. Previously each scaling decision cost days to weeks of training, and the architecture dimensions are coupled, so they cannot be enlarged independently. Axia maps embedding dimension, interaction depth and interaction width through a scaling layer into complete architectures, then uses Bayesian surrogates to pick a small number of training trials. Across 34 model iterations in two development cycles, offline normalized entropy improved by a mean 0.66% relative and the online key metric by a cumulative 0.36%.