Can Diffusion Replace Autoregressive LLMs? The Complete LLaDA 2.X Guide
From DDPM to LLaDA 2.1 -- everything about diffusion-based LLMs. Masked Diffusion, Token Editing, and MoE scaling dissected across 4 parts.
Can Diffusion Replace the LLM? A Complete Anatomy of LLaDA 2.X
ChatGPT, Claude, Gemini — every large language model (LLM) we use today is built on a single principle. Autoregressive (AR) generation: left to right, one token at a time, predicting the next word.
This approach works remarkably well. But it has structural limitations.
- Tokens must be produced one at a time in sequence, making parallel generation impossible
- Even if the model knows "A is B," it cannot infer "B is A" — the Reversal Curse
- Because it only looks left to right, it cannot leverage right-side context
But what if we built an LLM using Diffusion?
Just as Stable Diffusion and DALL-E demonstrated in image generation — starting from noise and progressively refining toward a clean result — what if we could apply the same Diffusion approach to text?
In February 2025, a research team from HKU/PKU published LLaDA (Large Language Diffusion with mAsking), turning this possibility into reality. Then in late 2025, Ant Group's InclusionAI scaled up to 100B parameters with LLaDA 2.0, and in February 2026, LLaDA 2.1 solved the speed problem with an innovation called Token Editing.
This series fully dissects everything from the fundamentals of Diffusion to the latest techniques in LLaDA 2.1, across 4 parts.
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