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parallel decoding

topic5 events
papersTODAY 04:00 UTC

Temporal Self-Distillation Speeds Up Discrete Diffusion Language Models

A new arXiv paper proposes Temporal Self-Distillation, a training method aimed at discrete diffusion language models that generate several tokens at once. Such models lose quality when too many tokens are decoded in parallel, and the technique is presented as a simple way to reduce that degradation. The approach targets faster inference without the accuracy drop that usually accompanies aggressive parallel decoding.

diffusion-models/inference/distillation/research
papersTODAY 04:00 UTC

LLaDA-UI Applies Block-wise Diffusion Decoding to Vision-Language GUI Agents

A new arXiv paper introduces LLaDA-UI, a method that adapts diffusion large language models to vision-language agents that operate graphical user interfaces. Diffusion decoding generates tokens in parallel blocks and in arbitrary order, which the authors argue suits latency-sensitive GUI tasks. The work positions interface agents as a testbed for this alternative to standard left-to-right generation.

gui-agents/diffusion-models/vision-language/arxiv
papersTODAY 04:00 UTC

Coupled-Noise Distillation Method Targets One-Step Block Generation in Diffusion Language Models

A revised arXiv paper examines why masked diffusion language models can produce incoherent text blocks: they decode every position in a block in parallel from separate marginal distributions. The authors propose a coupled-noise distillation approach intended to let such models generate a whole block in a single step while keeping the tokens mutually consistent. The work is a research preprint and has not been peer-reviewed.

diffusion-models/language-models/distillation/research
papersSEP 12 04:00 UTC

Logit Refiner Targets Intra-Scale Dependencies in Visual Autoregressive Models

A new arXiv paper analyzes visual autoregressive models, which generate images by predicting one scale of tokens at a time and emitting all tokens in a scale in parallel. The authors argue this parallel decoding acts as a mean-field-style approximation that drops spatial dependencies within each scale. They propose a Logit Refiner method that models these intra-scale relationships to improve generation quality.

image-generation/autoregressive/research/generative-models