dLLM: Simple Diffusion Language Modeling

TL;DR

dLLM is an open-source framework that standardizes and simplifies the training, inference, and evaluation of diffusion language models, facilitating reproducibility, customization, and extension in the field.

Contribution

It introduces a unified, flexible framework for diffusion language models, enabling easy reproduction, fine-tuning, deployment, and creation of small models from scratch.

Findings

Standardized pipeline for diffusion language models

Reproducible recipes for small DLMs from scratch

Released checkpoints to accelerate research

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.