Get Started
Home
Topics
Search
Library
Research questionHow can continuous diffusion language models reduce denoising steps without sacrificing text-generation quality?Continuous diffusion language models generate text through repeated denoising steps, which can make inference too slow for practical use. Reducing the number of steps may also degrade the quality of generated language.
AI
Code Generation & Program Synthesis
Diffusion Models
Evaluation & Benchmarks
Inference Optimization
Machine Learning
Natural Language Processing
Latest papersRecent research connected to this question, newest first.Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or OneThe evidence concerns PlaidQ, a 0.7B continuous diffusion language model for code generation. Reported results use HumanEval and MBPP+ pass rates, including few-step and single-step distilled models; broader language tasks and deployment conditions are not established.research paper · Sep 3, 2026DLM-One: Diffusion Language Models for One-Step Sequence GenerationThe evidence concerns continuous diffusion language models operating in token-embedding and logit-simplex spaces, with experiments across multiple representative models and benchmark text-generation tasks. It reports one-step generation results, analyzes language-domain distillation failure modes, and examines an adversarially regularized two-stage training scheme; the reported speedups and quality are experimental.research paper · Sep 2, 2026
Related questions
How can masked diffusion language models retain generation quality with only a few denoising steps?How can few-step diffusion defer high-resolution denoising without causing transition artifacts?How should diffusion samplers allocate limited neural evaluations without sacrificing generated image quality?How can diffusion language models adapt infilling span lengths without sacrificing quality or inference speed?