By Marianne Arriola, Aaron Gokaslan, Justin T Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov

We introduce BD3-LMs, a family of Block Discrete Denoising Diffusion Language Models that achieve SOTA likelihoods among diffusion models and enable generation of arbitrary-length sequences. BD3-LMs combine the strengths of autoregressive and diffusion language models by decomposing a token sequence into blocks and performing discrete diffusion within each block. By tuning the block size, we interpolate between autoregressive and diffusion models which introduces a trade-off between quality and sample efficiency. We propose a recipe of building effective BD3-LMs that includes an efficient training algorithm, estimators of gradient variance, and data-driven noise schedules to minimize the variance.

This is a retrained baseline model from MDLM. Differently from Sahoo et. al, we train our MDLM baseline on OpenWebText without injecting BOS/EOS at the beginning/end of the training context. This allows us to generate sequences longer than 1024 tokens at inference.

See our GitHub README, where we provide sample scripts for training, likelihood evaluation, and generation.

@inproceedings{
arriola2025block,
title={Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models},
author={Marianne Arriola and Aaron Gokaslan and Justin T Chiu and Zhihan Yang and Zhixuan Qi and Jiaqi Han and Subham Sekhar Sahoo and Volodymyr Kuleshov},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://arxiv.org/abs/2503.09573}
}