GoKawiilExplainer breaks down how diffusion-based language models are built
A technical walkthrough details how diffusion language models work as an alternative to the dominant autoregressive approach used by most LLMs today. Rather than generating text token-by-token in sequence, diffusion models produce an entire sequence at once and refine it over multiple steps, drawing on techniques like masking, iterative refinement, and post-training used in recent open-source models. The material stems from workshop talks and lectures given at ICLR 2026 and MLSS 2026.
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Autoregressive generation, while dominant, is inherently slow and cannot fix early errors since tokens are locked in once produced. Diffusion offers a path to faster, parallelizable generation with built-in error correction and full bidirectional context, which could reshape how future text and code generation systems are designed.
- Diffusion models generate entire sequences at once and refine them iteratively, unlike token-by-token autoregressive models.
- This approach allows tradeoffs between speed and quality and enables correction of earlier mistakes during generation.
- The explainer is based on academic talks from ICLR 2026 and MLSS 2026, covering masking, refinement, and post-training techniques.
Probabilistic Machine Learning: Advanced Topics (Kevin P. Murphy) — If this walkthrough of masking diffusion and iterative refinement left you wanting the underlying math, Murphy's advanced volume covers generative modeling, including diffusion, in one rigorous reference. It's a great desk companion for working through the derivations behind diffusion language models rather than just the code.
See Probabilistic Machine Learning: Advanced Topics (Kevin P. Murphy) on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: kuleshov-group.github.io — Volodymyr Kuleshov, 2026-08-30
Published there as: “How to build a diffusion language model”
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