This ICML'24 paper demonstrates that classical deep learning recommendation models (DLRMs) can be reformulated as generative modeling problems called Generative Recommenders (GRs). The work proposes efficient algorithms like HSTU and M-FALCON to accelerate training and inference for large-scale sequential models by 10x-1000x, and shows scaling laws for deployed recommendation systems at billion-user scale.