dLLM: Simple Diffusion Language Modeling
Zhanhui Zhou, Lingjie Chen, Hanghang Tong University of Illinois at Urbana-Champaign, Dawn Song University of California Berkeley
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.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表