Making Diffusion LLMs Faster: A Survey of Speedup Techniques
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- Education
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- ARTICLE
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- @juliarturc
- Added
- Jul 28, 2026
About
A video walkthrough of methods researchers use to speed up diffusion-based language models, covering self-distillation through time, curriculum learning, confidence-based unmasking, guided diffusion (FlashDLM), approximate KV caching (dLLM-Cache, dKV-Cache), and block diffusion. It links these techniques to specific papers and models like LLaDA, Seed Diffusion, and Mercury, framing them against the standard autoregressive LLM paradigm.
Why it made the leaderboard
If you're tracking diffusion LLMs as a lower-latency alternative to autoregressive decoding, this maps the whole speedup landscape — caching tricks, unmasking schedules, block diffusion — onto the specific papers and shipping models that use them, so you can tell which claimed speedups actually transfer to your workload.
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