@inproceedings{zhou-etal-2026-dllm,
    title = "d{LLM}: Simple Diffusion Language Modeling",
    author = "Zhou, Zhanhui  and
      Chen, Lingjie  and
      Tong, Hanghang  and
      Song, Dawn",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.8/",
    pages = "78--88",
    ISBN = "979-8-89176-392-0",
    abstract = "Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad-hoc research codebases or lack transparent implementations, making them difficult to reproduce or extend. As the field accelerates, there is a clear need for a unified framework that standardizes these common components while remaining flexible enough to support new methods and architectures.To address this gap, we introduce dLLM, an open-source framework that unifies the core components of diffusion language modeling{---}training, inference, and evaluation{---}and makes them easy to customize for new designs. With dLLM, users can reproduce, finetune, deploy, and evaluate open-source large DLMs such as LLaDA and Dream through a standardized pipeline.The framework also provides minimal, reproducible recipes for building small DLMs from scratch with accessible compute{---}including converting any BERT-style encoder or autoregressive LM into a DLM. We also release the checkpoints of these small DLMs to make DLMs more accessible and accelerate future research."
}