Scaling Diffusion Language Models via Adaptation from Autoregressive
  Models
Authors: name: Shansan Gong, name: Shivam Agarwal, name: Yizhe Zhang, name: Jiacheng Ye, name: Lin Zheng, name: Mukai Li, name: Chenxin An, name: Peilin Zhao, name: Wei Bi, name: Jiawei Han, name: Hao Peng, name: Lingpeng Kong

Diffusion Language Models (DLMs) have emerged as a promising new paradigm for
text generative modeling, potentially addressing limitations of autoregressive
(AR) models. However, current DLMs have been studied at a smaller scale
compared to their AR counterparts and lack fair comparison on language modeling
benchmarks. Additionally, training diffusion models from scratch at scale
remains challenging. Given the prevalence of open-source AR language models, we
propose adapting these models to build text diffusion models. We demonstrate
connections between AR and diffusion modeling objectives and introduce a simple
continual pre-training approach for training diffusion models. Through
systematic evaluation on language modeling, reasoning, and commonsense
benchmarks, we show that we can convert AR models ranging from 127M to 7B
parameters (GPT2 and LLaMA) into diffusion models DiffuGPT and DiffuLLaMA,
using less than 200B tokens for training. Our experimental results reveal that
these models outperform earlier DLMs and are competitive with their AR
counterparts. We release a suite of DLMs (with 127M, 355M, and 7B parameters)
capable of generating fluent text, performing in-context learning, filling in
the middle without prompt re-ordering, and following instructions
https://github.com/HKUNLP/DiffuLLaMA.