How to Build a Diffusion Language Model: A Complete Guide
zainhas · x · 2026-08-31
The Kuleshov Group released a comprehensive guide on building Diffusion Language Models.
- Comparison: Contrasts mainstream autoregressive models (left-to-right, single token, no error correction) with diffusion models (generate full sequence at once, iterative refinement).
- Key Advantages:
- Allows error correction during generation.
- Trade-off speed and quality by adjusting steps.
- Uses bidirectional context at every step.
- Content: Covers simple masking diffusion, iterative refinement, post-training, and variable-length generation techniques.
Related event: Diffusion LLMs Can Fix Mistakes and Generate 5-10x Faster(2 posts)→
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