{"slug": "autoregressive-vs-diffusion-a-different-way-ai-could-generate-text", "title": "Autoregressive vs Diffusion: A Different Way AI Could Generate Text", "summary": "Rijul, a developer building the AI code review tool LiveReview, outlined the difference between autoregressive and diffusion-based text generation. Autoregressive models, used by most deployed language models today, generate one token at a time from left to right, while diffusion approaches such as LLaDA, Dream-7B, and Mercury start from multiple masked positions and iteratively refine the sequence, potentially enabling more parallel processing. The developer noted that diffusion remains an emerging approach with performance varying across models and tasks.", "body_md": "*Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. [Star us](https://github.com/HexmosTech/LiveReview/) to help devs discover the project, give it a try, and share your feedback to help improve the product.*\n\nWhen you are using AI tools, it is useful to know that there are different ways an AI model can generate text.\n\nToday, most deployed language models use one main approach, but other approaches are emerging as well.\n\nHere, I'll introduce you to two different approaches to generating text.\n\nThe first is **autoregressive generation**.\n\nThe way autoregressive generation works is quite simple.\n\nThe model generates **one token at a time**, moving from **left to right**.\n\nFor example, if we ask the model:\n\n```\nThe cat is sitting on the ___\n```\n\nThe model might first predict:\n\n```\nmat\n```\n\nThen it uses everything generated so far to predict what comes next.\n\nSo the process looks something like:\n\n```\nThe\n   ↓\nThe cat\n   ↓\nThe cat is\n   ↓\nThe cat is sitting\n   ↓\nThe cat is sitting on\n   ↓\nThe cat is sitting on the mat\n```\n\nThis is the approach used by most deployed language models today.\n\nThe important thing to notice is that the process is **sequential**.\n\nThe model needs to generate one part before it can move on to the next.\n\nOnce a token has been generated, the model doesn't normally go back and revise it as part of that same generation process.\n\nThis also limits parallelism because the next token depends on the tokens that came before it.\n\nNow, let's look at an emerging approach that works quite differently.\n\nDiffusion is more like **sculpting**.\n\nImagine you are making a statue. You start with a rough shape and then keep refining different parts until you get the final result.\n\nDiffusion-based text generation follows a somewhat similar idea.\n\nInstead of starting from the first token and generating one token after another, some diffusion language models can start with a sequence containing multiple masked or uncertain positions.\n\nFor example:\n\n```\nThe [MASK] is [MASK] on the [MASK].\n```\n\nThe model can work on multiple positions rather than being forced to complete them strictly from left to right.\n\nFor example, multiple positions could be refined in the same step:\n\n```\nThe [MASK] is [MASK] on the [MASK].\n      ↓           ↓           ↓\nThe cat is sleeping on the mat.\n```\n\nBut the process doesn't necessarily stop there.\n\nThe model can revisit and refine parts of the sequence based on the surrounding context.\n\n```\nThe cat is sleeping on the mat.\n             ↓\n          revisit\n             ↓\nThe cat is sitting on the mat.\n```\n\nHere, the model could change an earlier prediction:\n\n```\nsleeping → sitting\n```\n\nThe exact way this refinement works depends on the diffusion model, but the key idea is that the model can iteratively refine the sequence rather than only moving forward from left to right.\n\nSo the process is less like:\n\n```\nToken 1 → Token 2 → Token 3 → Token 4 → ...\n```\n\nand more like:\n\n```\nMultiple uncertain positions\n          ↓\n   Fill / refine them\n          ↓\n Revisit uncertain parts\n          ↓\n      Refine again\n          ↓\n     Final sentence\n```\n\nThis also creates opportunities for more parallel processing, since multiple positions can potentially be worked on at the same time.\n\nSome models exploring this approach include **LLaDA, Dream-7B, and Mercury**.\n\nSince this is still an emerging approach, performance varies across models and tasks, while autoregressive generation remains the dominant approach today.\n\nSo why are people interested in this approach?\n\nOne potential advantage is **parallelism**.\n\nIf multiple parts of the sequence can be processed at the same time, diffusion-based generation could potentially produce responses faster.\n\nIt could also potentially reduce the cost of generation if that parallelism can be used effectively.\n\nOf course, this doesn't mean diffusion is automatically faster or cheaper in every situation. The actual performance depends on how the model and inference system are designed.\n\nYou can think of the two approaches like this:\n\n**Autoregressive:** Write the sentence one word at a time, from left to right.\n\n**Diffusion:** Rough out different parts of the sentence and keep refining them until the final version emerges.\n\nThat's the fundamental difference in how they approach text generation.\n\nThere are still many new developments happening in how AI generates text.\n\nAutoregressive generation is the dominant approach today, but diffusion-based language models are exploring a different way of generating text.\n\nAnd the interesting part is that this area is still evolving.\n\nWe are already seeing newer ideas emerge, such as **soft masking**, which takes the idea of masking and makes it more flexible.\n\nSo, even something as fundamental as **how an AI writes a sentence** is still being explored.\n\nYour team's attention is limited, and the deluge of AI-generated code is making it harder to keep production reliable and secure without slowing you down.\n\nI'm building **LiveReview**, a blast-radius aware AI code review built for your business-critical systems.\n\nInstead of presenting every diff with equal emphasis, **LiveReview scores each change by blast radius — how far its impact reaches through your call graph — so you can focus attention where it actually matters.**\n\nSpend code review effort where business risk is highest — not spread evenly across every diff.\n\n⭐ Star it on GitHub: \n\nLiveReview is an AI code reviewer that scores every hunk of a diff by **blast radius**: how far a change reaches through your call graph, how much persistent state it touches, and how well-tested it is. A 3-line change to a shared auth check can outrank a 300-line UI tweak. Your team's attention goes to the highest-risk code first, not spread evenly across every diff.\n\n*LiveReview's Blast Radius & Review Priority scoring, live in the diff viewer.*\n\n| The exact math, not a black box | Visualize blast radius at a glance | Every factor that feeds the score | \n|---|---|---|\n\n**Here's the goal:**\n\n**Click below to try LiveReview with your codebase:**", "url": "https://wpnews.pro/news/autoregressive-vs-diffusion-a-different-way-ai-could-generate-text", "canonical_source": "https://dev.to/rijultp/autoregressive-vs-diffusion-a-different-way-ai-could-generate-text-4c9m", "published_at": "2026-09-16 18:13:26+00:00", "updated_at": "2026-09-16 18:40:57.319447+00:00", "lang": "en", "topics": ["large-language-models", "generative-ai", "natural-language-processing", "ai-research"], "entities": ["Rijul", "LiveReview", "HexmosTech", "LLaDA", "Dream-7B", "Mercury"], "alternates": {"html": "https://wpnews.pro/news/autoregressive-vs-diffusion-a-different-way-ai-could-generate-text", "markdown": "https://wpnews.pro/news/autoregressive-vs-diffusion-a-different-way-ai-could-generate-text.md", "text": "https://wpnews.pro/news/autoregressive-vs-diffusion-a-different-way-ai-could-generate-text.txt", "jsonld": "https://wpnews.pro/news/autoregressive-vs-diffusion-a-different-way-ai-could-generate-text.jsonld"}}