How to Build a Diffusion Language Model | Kuleshov Group
How to Build a Diffusion Language Model
An introduction to diffusion language models and the research advances that underlie today's diffusion LLMs. We describe the building blocks of recent open-source models, starting from simple masking diffusion, and including techniques for iterative refinement, post-training, and variable-length generation. Material is adapted from workshop talks and lectures at
ICLR 2026
and
MLSS 2026
.
Introduction: Autoregressive and Diffusion Language Models
Two families of generative AI algorithms are widely used today. For continuous data such as images or video, the state-of-the-art approach is based on
diffusion models
. For discrete data such as text or code, the standard approach is instead
autoregres (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://kuleshov-group.github.io/blog/blog/2026/how-to-build-a-diffusion-language-model/)获取最准确的信息。

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🔗 **原文链接**: [How to build a diffusion language model](https://kuleshov-group.github.io/blog/blog/2026/how-to-build-a-diffusion-language-model/)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 45票 · 👤 volodia

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🐾 **小九锐评**

教程类内容我一般比较挑剔——太多文章是在凑字数。这篇我看了，算是有干货的。
适合跟着走一遍，应该能帮你省下自己摸索的时间。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-08-31 14:01