arXiv Machine Learning
DUEL: Exact Likelihood Reshapes Masked Diffusion Scaling Laws and Beyond
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Jul 28
UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective
arXiv:2607. 24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling.
arXiv Machine Learning
Oct 8
CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models
CoDR (Confidence Drift Remasking) is a training‑free, sampler‑agnostic refinement pass for masked diffusion language models that detects and corrects early token commitments that become unsupported as context grows denser. It estimates confidence drift for all committed positions using only a few forward passes and remasks only the tokens whose confidence has dropped, thereby improving accuracy across multiple backbones, tasks, and samplers with modest computational overhead.
arXiv Machine Learning
Jun 2
BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers
arXiv:2606. 02241v1 Announce Type: new Abstract: Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion?
arXiv AI
Jul 24
Understanding and Accelerating the Training of Masked Diffusion Language Models
arXiv:2605. 13026v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling.
arXiv Machine Learning
Sep 25
Enabling Approximate Joint Sampling in Diffusion LMs
The paper introduces a lightweight single‑layer sampler that allows masked diffusion language models to approximate joint sampling of multiple tokens in a single full‑model forward pass. By training the sampler to mimic exact joint sampling from a frozen diffusion model, the authors enable parallel unmasking of tokens while maintaining a close match to the true joint distribution. Experiments on Dream‑7B and Llada‑7B models show that unmasking four tokens per denoising step yields a MAUVE score of 0.87, a substantial improvement over the marginal baseline of 0.31.
arXiv AI
Jul 29
CaRE Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models
arXiv:2607. 24763v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) are advancing rapidly, yet the evaluation standards needed to reliably interpret their progress have not kept pace.