Trillion-Parameter Sequential Transducers for Scalable Generative Recommendations

Generative Recommenders (GRs) reformulate recommendation tasks as sequential transduction problems, enabling the training and deployment of trillion-parameter models that significantly outperform traditional Deep Learning Recommendation Models (DLRMs) in large-scale industrial settings.