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.

The search for "paper: 405rar" refers to , a recent paper published in November 2024 that introduces a new state-of-the-art model for image generation. Overview of RAR 405rar

: On the ImageNet-256 benchmark, RAR achieved a FID score of 1.48 , which is a significant improvement over previous autoregressive generators and even outperforms many top-tier diffusion-based and masked transformer models. : It introduces a randomness annealing strategy with

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. Overview of RAR : On the ImageNet-256 benchmark,

: A suite released in April 2024 to evaluate how well retrieval models can perform reasoning tasks typically reserved for Large Language Models (LLMs).

: The paper and its associated codebase are available through platforms like arXiv and GitHub . Related Benchmarks & Agents