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#masked diffusion

dLLM: Simple Diffusion Language Modeling

Intermediate

Zhanhui Zhou, Lingjie Chen et al.Feb 26arXiv

dLLM is a single, open-source toolbox that standardizes how diffusion language models are trained, run, and tested.

#diffusion language models#masked diffusion#block diffusion

T3D: Few-Step Diffusion Language Models via Trajectory Self-Distillation with Direct Discriminative Optimization

Intermediate

Tunyu Zhang, Xinxi Zhang et al.Feb 12arXiv

This paper shows how to make diffusion language models write high‑quality text in just a few steps, which makes them much faster.

#diffusion language models#few-step decoding#trajectory self-distillation

Balancing Understanding and Generation in Discrete Diffusion Models

Intermediate

Yue Liu, Yuzhong Zhao et al.Feb 1arXiv

This paper introduces XDLM, a single model that blends two popular diffusion styles (masked and uniform) so it both understands and generates text and images well.

#XDLM#discrete diffusion#stationary noise kernel

On the Role of Discreteness in Diffusion LLMs

Intermediate

Ziqi Jin, Bin Wang et al.Dec 27arXiv

The paper asks what a truly good diffusion-based language model should look like and lists five must-have properties.

#diffusion language models#smooth corruption#discrete tokens

Scaling Behavior of Discrete Diffusion Language Models

Intermediate

Dimitri von Rütte, Janis Fluri et al.Dec 11arXiv

This paper studies how a newer kind of language model, called a discrete diffusion language model (DLM), gets better as we give it more data, bigger models, and more compute.

#discrete diffusion#language models#scaling laws

LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Intermediate

Tiwei Bie, Maosong Cao et al.Dec 10arXiv

Before this work, most big language models talked one word at a time (autoregressive), which made them slow and hard to parallelize.

#diffusion language model#masked diffusion#block diffusion

Learning Unmasking Policies for Diffusion Language Models

Intermediate

Metod Jazbec, Theo X. Olausson et al.Dec 9arXiv

Diffusion language models write by gradually unmasking hidden words, so deciding which blanks to reveal next is a big deal for both speed and accuracy.

#diffusion language models#masked diffusion#unmasking policy