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Why Diffusion is the Future of Text Generation


This content originally appeared on Level Up Coding – Medium and was authored by Mohit Sewak, Ph.D.

A comprehensive case for why text diffusion models represent the next major paradigm shift in AI

Autoregressive models build text like a bricklayer — one word at a time. Diffusion models sculpt it — refining the whole piece at once.

The Cold Open: The Bricklayer with a Stutter

We’ve all seen it. You’re asking an AI to write a story, and somewhere around the third paragraph, it completely forgets the main character’s name. Or it starts repeating itself, like a glitching NPC in a video game. It’s like talking to a brilliant friend who, after three sentences, gets distracted by a shiny object and starts talking about squirrels.

This happens because today’s dominant AI models, known as autoregressive (AR) models, are essentially hyper-fast, hyper-smart bricklayers. They build sentences one brick — one word — at a time, from left to right (Vaswani et al., 2017). Each new brick is placed based on the ones that came before it. It’s a simple, powerful idea. But it’s also a trap.

The bricklayer’s biggest problem is that it can never go back. If it lays a crooked brick at the beginning of the wall, the rest of the wall is going to be crooked. It can’t fix the foundation once the fifth floor is built. This leads to compounding errors (Ranzato et al., 2015) and that dreaded loss of long-term memory. And because it has to lay every single brick in order, it’s fundamentally slow. The longer the wall, the longer it takes. One brick at a time.

Now, what if, instead of laying bricks, we could sculpt the text?

Enter diffusion models. This is a totally different beast. It’s a sculptor. It starts with a shapeless block of marble — pure, random noise — and over dozens of steps, it meticulously chips away, refines, and polishes until a perfectly formed sentence, paragraph, or document emerges. It sees the whole sculpture at every stage of the process. A mistake on the foot can be fixed while working on the head.

This holistic approach is poised to smash the core limitations of our trusty old bricklayers. And this isn’t some fringe idea from a dusty academic lab anymore. The big guns are all in. Google is pushing its Gemini Diffusion, and Microsoft Research has been making huge breakthroughs with models like TREC (Liu et al., 2024).

The era of the bricklayer is coming to an end. The age of the sculptor is just beginning.

“The future is not a destination. It is a direction.” — Douglas Engelbart

The Big Deal: Why We Need Sculptors, Not Just Faster Bricklayers

The sequential nature of autoregressive models creates a bottleneck. Diffusion’s parallel process unlocks speed and global coherence.

So, the bricklayers are a little clumsy with long stories. What’s the big deal?

The big deal is that our demand for AI is evolving. We don’t just want it to write essays anymore. We want real-time, lightning-fast partners. Imagine a customer service bot that takes 30 seconds to form a reply, or a live translation app that’s always a sentence behind. The bricklayer’s one-word-at-a-time process is a speed bump on the highway to the future.

And then there’s the coherence problem. For writing anything longer than an email, our bricklayer models can, to put it technically, “lose the plot.” Their inability to maintain a global vision makes them terrible novelists.

This is why the “why now” is so critical. When giants like Google and Microsoft start throwing their weight behind a new technology, you know it’s not just an experiment. It’s a signal that the entire industry is about to pivot. They see that sculpting, not bricklaying, is the key to unlocking the next level of AI applications: systems that are not just fluent, but fast, consistent, and controllable.

So, how in the world does this sculpting magic work? How do you turn TV static into Shakespeare? Let’s get our hands dirty.

ProTip:
The core idea of diffusion isn’t new! It was first proposed way back in 2015, drawing inspiration from thermodynamics (Sohl-Dickstein et al., 2015). It just took the wild success of image models like DALL-E 2 and Stable Diffusion to make everyone realize its full potential.

The Sculptor’s Studio: From Blurry Noise to Polished Prose

Diffusion models work by iterative refinement, starting with random noise and gradually “denoising” it into coherent text over multiple steps.

If you’ve seen an AI art generator at work, you’ve seen diffusion. It starts with a canvas full of what looks like static. Then, step by step, a vague shape appears. The shape becomes a silhouette. The silhouette gets details. After 50 or so steps, that random noise has been refined into a photorealistic astronaut riding a horse.

That’s the core concept: iterative refinement. The model learns to denoise an image, gradually reversing the process of turning a clear picture into static (Ho et al., 2020).

Easy enough for images, right? Pixels have numerical values. You can add a little bit of numerical “noise” to them and it makes sense. But how do you do that with words?

This was the great puzzle, the “Discrete-Continuous Chasm.” Words are discrete, distinct things. You can’t have “half a word.” You can’t add 10% noise to the word ‘cat’ and have it slowly morph into ‘dog’. It’s either ‘cat’ or it’s not. This chasm seemed unbridgeable, and it’s why for years, diffusion was an image-only party.

But the AI world is full of clever people who love a good challenge. They came up with not one, but three ingenious ways to build a bridge across this chasm.

Bridging the Chasm: Three Schools of AI Wizardry

Researchers devised three clever strategies to bridge the gap between the continuous world of noise and the discrete world of words.

Imagine our AI researchers are three teams of adventurers trying to cross a canyon. Each team comes up with a different, brilliant solution.

1. The Translator: Diffusion in a Secret Language

The first team, the creators of models like Diffusion-LM (Li et al., 2022), decides not to carry the words across the chasm at all. Instead, they translate them.

  • Step 1: Embed. They take each word (‘cat’, ‘dog’) and translate it into a long list of numbers — a vector. This is its “embedding,” a mathematical representation of its meaning.
  • Step 2: Diffuse. Now that everything is numbers (a continuous space), they can run the standard image-style diffusion process. They turn the number-sentence into numerical noise and then train a model to reverse it.
  • Step 3: Decode. Once they have a final, denoised set of numbers, they translate it back, finding the closest word in their dictionary for each number-vector.

Analogy: It’s like translating a book into French to edit it, then translating it back to English. It works! But you risk “lost in translation” errors. The final number-vector might not perfectly match any word, forcing a rounding error that could change the whole meaning.

2. The Purist: Building the Bridge Brick by Word-Brick

The second team, behind models like GENIE (Reid et al., 2023), scoffs at the translators. “Translation errors? Unacceptable!” They decide to redefine the whole concept of diffusion to work directly with words.

Instead of adding “noise,” their forward process involves randomly replacing words. A word might be swapped with another random word or a blank [MASK] token, based on a carefully designed probability schedule (Austin et al., 2021). The model then learns to reverse this process, predicting the original words from the corrupted text.

Analogy: This is like editing the book directly in its original English. It’s harder, requires more specialized tools (the math is more complex), but you completely avoid translation errors. It’s a cleaner, more direct approach.

3. The Elegant Compromise: Don’t Move the Words, Move the Idea of the Words

The third team, the geniuses behind models like SSD-LM (Huang et al., 2023), finds a stunningly clever middle path. They realize they don’t need to diffuse the words or their numerical translations. They can diffuse the probability of each word.

Here’s how it works: for each position in a sentence, you start with a 100% chance of a specific word (e.g., “The [100% cat] sat…”). The “noising” process gradually flattens this, until every word in the dictionary has an equal, tiny chance of being there (e.g., “[0.001% cat], [0.001% dog], [0.001% car]…”). The model then learns to reverse this, a king the fuzzy, uncertain probabilities and sharpening them back into confident, 100% choices.

Analogy: Instead of editing the words themselves, you edit the author’s probabilistic notes on which word they are most likely to use at each point. You get the power of continuous math without any messy rounding errors. This is the best of both worlds and is currently one of the most popular strategies.

Trivia:
The “probability space” these models work in is called a simplex. It’s a geometric shape that represents all possible probability distributions. So when you hear “simplex-based diffusion,” you know you’re dealing with this elegant third approach.

The Training Montage: From Contender to Champion

Like a prize fighter in training, text diffusion models underwent a series of key breakthroughs to become faster, smarter, and more stable.

Okay, so our sculptor has a method. But just having a chisel doesn’t make you Michelangelo. Early text diffusion models were promising but clumsy. They needed a Rocky-style training montage to become real contenders. Three key breakthroughs got them there.

Innovation 1: Learning to Self-Correct (TREC)

A huge problem was that the models needed to use their own partial sculptures to guide the next chip of the chisel. But they were bad at it. During training, they could peek at the final answer, but during a real match, they had to rely on their own imperfect work, which threw them off.

This is where Microsoft’s TREC model changed the game (Liu et al., 2024). They used a technique from my world of cybersecurity and martial arts: reinforcement learning. It’s like a sensei not just showing a student a kick, but rewarding them for correcting their own balance mid-kick. TREC taught the model how to effectively use its own predictions as a guide, making the whole process dramatically more stable and accurate. It learned how to check its own homework.

Innovation 2: Getting Faster (Efficient Samplers)

The sculptor was good, but slow. Taking 1000 tiny refinement steps to write one sentence is not practical. The next breakthrough came from developing smarter chisels. Researchers developed efficient samplers (like the famous DDIM) that allowed the model to take bigger, more intelligent steps (Song et al., 2020). Instead of sanding the marble a grain at a time, it could now confidently chip away larger chunks, reducing the number of steps from 1000 down to 50, or even 20, without a major drop in quality.

Innovation 3: A Better Training Philosophy (New Objectives like SEDD)

Finally, researchers like those behind the SEDD model (Ye et al., 2024) went back to the drawing board on the fundamental mathematics. They designed new training goals (objectives) that gave the model a clearer, more stable signal of what it was supposed to be learning. This is the equivalent of giving your fighter a better diet and sports psychology. It’s not about new moves; it’s about making the fundamentals stronger.

The Showdown: Autoregressive vs. Diffusion

When pitted head-to-head, diffusion models show clear advantages in speed, coherence, and controllability over their autoregressive counterparts.

So, the training montage is over. Our sculptor is ripped and ready. How does it stack up against the reigning champ, the bricklayer?

  • Inference Speed: For long texts, it’s not even close. The bricklayer takes N steps for N words. The sculptor takes K steps for the whole text. If K (say, 50 steps) is much smaller than N (a 500-word paragraph), the sculptor wins on speed, hands down.
  • Global Coherence: Clear win for Diffusion. By working on the whole text at every step, the sculptor has a massive structural advantage. It can ensure the beginning of the story matches the end because it’s refining both simultaneously.
  • Controllability & Flexibility: Clear win for Diffusion. The iterative process is a dream for control. Want to fill in a blank in the middle of a sentence ([____])? A bricklayer would have to tear down and rebuild the wall. A sculptor just works on that specific spot. This is called text infilling, and diffusion models are naturals at it.
  • Error Resilience: Clear win for Diffusion. The sculptor can self-correct. If an early step suggests a bad word, later steps can revise it as more context becomes clear. The bricklayer’s mistakes are permanent.
“The best way to predict the future is to invent it.” — Alan Kay

The Path Forward: Avengers, Assemble!

So, is the bricklayer obsolete? Not so fast. The future isn’t a zero-sum game. The most exciting frontier isn’t about one model defeating the other; it’s about them teaming up.

  • The Scaling Question: We know that making bricklayer models bigger makes them smarter in predictable ways (Kaplan et al., 2020). Does the same hold true for sculptors? That’s the billion-dollar question researchers are racing to answer.
  • The Ultimate Goal: The holy grail is “few-step” generation — getting perfect text in less than 10 steps. This would unlock the full speed advantage for real-time everything.
  • The Emerging Answer: Hybrid Models. This is the coolest part. The future is hybrid.
  • Draft and Polish: A diffusion model (the sculptor) can generate a fast, globally coherent “first draft,” and then a small, fast AR model (a specialist bricklayer) can do a quick “polishing pass” for local grammar and flow. SSD-LM already incorporates this idea (Huang et al., 2023).
  • Plan and Execute: A diffusion model can act as a “planner,” creating a high-level outline or semantic blueprint. Then, an AR model can act as the writer, “fleshing out” that plan into beautiful prose. This is the approach of the PLANNER model (Han et al., 2023).
It’s not AR vs. Diffusion. It’s AR and Diffusion.

The future isn’t a battle, but a partnership. Hybrid models that combine the strengths of both architectures are the next frontier.

Conclusion: The Dawn of the Sculptor

Let’s finish that tea. Here’s the takeaway: The autoregressive paradigm, the mighty bricklayer that built the modern AI world, has fundamental architectural limits. It’s slow, it makes irreversible errors, and it struggles with the big picture.

Diffusion models, the sculptors, are here to change that. By building text through a holistic process of iterative refinement, they directly attack those weaknesses.

The future of how we create with AI isn’t going to be a monoculture. It’s going to be a vibrant, diverse ecosystem. We’ll have pure bricklayers for some tasks, pure sculptors for others, and a powerful league of hybrid heroes for everything in between. The story of text diffusion is a classic tale of innovation, where inspiration from one world (images) completely upended another (language). It’s a paradigm shift, and it’s happening right now. The sculptors are in the studio, and they’re about to create some masterpieces.

References

Foundational Papers (Diffusion & Autoregressive)

Core Text Diffusion Architectures

Latent Space:

  • Gong, S., Li, M., Feng, J., Wu, Z., & Kong, L. (2023). DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. In International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2210.08933
  • Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., & Hashimoto, T. B. (2022). Diffusion-LM: Improving Paragraph-level Text Generation with Diffusion Models. arXiv preprint arXiv:2205.14217. https://arxiv.org/abs/2205.14217

Discrete State-Space:

Simplex-Based:

  • Huang, D. A., Gou, S., Varia, S., Bapna, A., Gorantla, V. C., Lin, C. H., … & Liao, H. (2023). SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model. In International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=6dK4T3-t3-6

Key Methodological Innovations

  • Liu, Y., Yang, T., Huang, S., Zhang, Z., Huang, H., Wei, F., … & Sun, F. (2024). Text Diffusion with Reinforced Conditioning. arXiv preprint arXiv:2402.14843. https://arxiv.org/abs/2402.14843
  • Song, J., Meng, C., & Ermon, S. (2020). Denoising Diffusion Implicit Models. In International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2010.02502
  • Ye, Z., Li, D., & Dai, B. (2024). Score Entropy Discrete Diffusion for text generation. In International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=T92DK61sHB

Hybrid Models and Future Directions

  • Han, C., Chen, J., He, J., & Tan, X. (2023). PLANNER: Generating Diversified Paragraphs with Planning-based Latent Diffusion Model. arXiv preprint arXiv:2305.07416. https://arxiv.org/abs/2305.07416
  • He, J., Chen, J., He, S., Zhou, M., & Tan, X. (2023). Block Discrete Denoising Diffusion for Flexible-Length Text Generation. arXiv preprint arXiv:2305.16101. [https://arxiv.org/abs/2305.16101]
  • Huang, D. A., Gou, S., Varia, S., Bapna, A., Gorantla, V. C., Lin, C. H., … & Liao, H. (2023). SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model. In International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=6dK4T3-t3-6

Surveys and Comparative Analyses

  • Lin, Z., Li, Y., Yang, Z., Zhang, Z., Zhou, B., & Su, J. (2024). A Survey of Diffusion Models in Natural Language Processing. arXiv preprint arXiv:2401.07188. https://arxiv.org/abs/2401.07188
  • Li, Y., Zhou, K., Zhao, W. X., & Wen, J. R. (2023). Diffusion models for non-autoregressive text generation: a survey. arXiv preprint arXiv:2303.06574. https://arxiv.org/abs/2303.06574

Disclaimer: The views and opinions expressed in this article are my own and do not necessarily reflect the official policy or position of any past or present employer. AI assistance was used in researching and drafting this article, as well as for generating the images. This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Why Diffusion is the Future of Text Generation was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.


This content originally appeared on Level Up Coding – Medium and was authored by Mohit Sewak, Ph.D.