Revisiting Autoregressive Models for Generative Image Classification

Class-conditional generative models have emerged as accu-rate and robust classifiers, with diffusion models demonstrating clear ad-vantages over other visual generative paradigms, including autoregressive(AR) models. In this work, we revisit visual AR-based generative clas-sifiers and identify an important limitation of prior approaches: theirreliance on a fixed token order, which imposes a restrictive inductivebias for image understanding. We observe that single-order predictionsrely more on partial discriminative cues, while averaging over multipletoken orders provides a more comprehensive signal. Based on this insight,we leverage recent any-order AR models to estimate order-marginalizedpredictions, unlocking the high classification potential of AR models.Our approach consistently outperforms diffusion-based classifiers acrossdiverse image classification benchmarks, while being up to 25× more ef-ficient. Compared to state-of-the-art self-supervised discriminative mod-els, our method delivers competitive classification performance – a no-table achievement for generative classifiers. The code and models areavailable at: https://github.com/yandex-research/ar-classifier.