Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation

ArXi:2603.07107v1 Announce Type: cross Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked lists. However, their practical deployment still faces two key challenges: (1) an inherent conflict between achieving high generation quality and ensuring low-latency inference, making it difficult to balance the two, and (2) insufficient interaction between user and item features in existing methods.