Text Diffusion LLMs Explained: D3PM and LLaDA Paper Walkthrough
www.youtube.com- Category
- Education
- Type
- ARTICLE
- Builder
- @juliarturc
- Added
- Jul 28, 2026
About
An educational video walkthrough explaining how diffusion-based language models (like Gemini Diffusion, Mercury Coder, and Seed Diffusion) generate text by refining a full draft in parallel rather than predicting tokens sequentially, offering roughly 10x faster inference than autoregressive models like GPT or Claude. It works through the D3PM and LLaDA papers as case studies, covering Markov chain formulations and diffusion in embedding vs. token space.
Why it made the leaderboard
If you keep hearing about Gemini Diffusion or Mercury Coder and want to understand why parallel draft-refinement can be ~10x faster than autoregressive decoding, this walks through the actual formulations (D3PM's Markov chain corruption, LLaDA's masked-token approach) instead of stopping at benchmark claims. It's the level of detail needed to judge whether diffusion LLMs are worth designing around.
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