arXiv Machine Learning
Alpha Diffusion Language Models: Factorization Alone Is Not the Problem
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 21
Breaking the Factorization Barrier in Diffusion Language Models
arXiv:2603. 00045v3 Announce Type: replace-cross Abstract: Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the ``factorization barrier'': the assumption that simultaneously predicted tokens are independent.
arXiv Computation and Language
4d ago
E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models
arXiv:2609.37533v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically...
arXiv Machine Learning
Aug 4
Just on Time: Token-Level Early Stopping for Diffusion Language Models
arXiv:2602. 11133v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability long before the final denoising step.
arXiv Computation and Language
Aug 28
Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference
The paper introduces Dependency-Aware Revocable Decoding (DARD), a training‑free framework for diffusion large language models that separates tokens into masked, candidate, and unmasked states. DARD verifies candidate tokens using a selective context that excludes less reliable tokens and adaptively regulates their influence on subsequent decoding. Experiments on 12 textual and multimodal benchmarks across three open‑source dLLMs show that DARD improves the speed‑quality Pareto frontier, achieving a 2.71× speedup and a 4.35‑point CIDEr gain over Saber on Flickr30K.
arXiv Machine Learning
Jun 5
A Survey on Diffusion Language Models
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
arXiv AI
Jun 2
EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models
arXiv:2606. 00722v1 Announce Type: cross Abstract: Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models are no exception.