A flurry of recent activity in the space of
continuous diffusion models for language
, after a few years of relative dormancy, suggests that this approach is making something of a comeback. Fully discrete diffusion methods had largely supplanted earlier attempts to make continuous diffusion work for language, but the tide is starting to turn. In this post, I want to take a closer look at what’s going on, and why it is happening now.
The recent influx of new research in this space inspired me to write up some of my thoughts. I have written about
diffusion language models
before, so this mainly serves as an update to cover everything that’s happened since then. This will be a fairly subjective account – other perspectives and dissenting opinions are very welcome in the comments and els (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://sander.ai/2026/08/24/continuous-dlms.html)获取最准确的信息。

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🔗 **原文链接**: [Continuous Diffusion Language Models (CDLM's)](https://sander.ai/2026/08/24/continuous-dlms.html)
🏷️ **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 · 👤 peter_d_sherman

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

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 08:01