It is important to distinguish the image generation model from other similarly named research:
RAR is an autoregressive (AR) image generator designed to be fully compatible with standard language modeling frameworks. It aims to bridge the gap between traditional AR models and more flexible bidirectional models like diffusion or masked transformers. 405rar
: It introduces a randomness annealing strategy with a permuted objective . This allows the model to learn bidirectional contexts—seeing different parts of the image simultaneously—without needing extra computational costs or changing the basic autoregressive structure. It is important to distinguish the image generation