We introduce Autoregressive Diffusion Models (ARDMs), a model class
encompassing and generalizing order-agnostic autoregressive models (Uria et
al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we
show are special cases of ARDMs under mild assumptions. ARDMs are simple to
implement and easy to train. Unlike standard ARMs, they do not require causal
masking of model representations, and can be trained using an efficient
objective similar to modern probabilistic diffusion models that scales
favourably to highly-dimensional data. At test time, ARDMs support parallel
generation which can be adapted to fit any given generation budget. We find
that ARDMs require significantly fewer steps than discrete diffusion models to
attain the same performance. Finally, we apply ARDMs to lossless compression,
and show that they are uniquely suited to this task. Contrary to existing
approaches based on bits-back coding, ARDMs obtain compelling results not only
on complete datasets, but also on compressing single data points. Moreover,
this can be done using a modest number of network calls for (de)compression due
to the model's adaptable parallel generation.

本文介绍了 AR 模型中的一种 Autoregressive Diffusion Models，相较于其他模型具有更高的性能表现。同时，作者还将该模型应用于无损压缩，并表明该模型在此任务上也表现出了极佳的结果，其适用于数据点的压缩及适应各种生成任务。