Diffusion Beats Autoregressive in Data-Constrained Settings(blog.ml.cmu.edu)
blog.ml.cmu.edu
Diffusion Beats Autoregressive in Data-Constrained Settings
https://blog.ml.cmu.edu/2025/09/22/diffusion-beats-autoregressive-in-data-constrained-settings/
15 comments
This paper was just too overhyped by the authors. Also, the initial evals were very limited and very strange. This blog post does a much better job at a similar observation -- goes into details and does proper evaluation (also better attribution): https://jinjieni.notion.site/Diffusion-Language-Models-are-S...
> This paper addresses the challenge by asking: how can we trade off more compute for less data?
There is evidence that training RNN models that compute several steps with same input and coefficients (but different state) lead to better performance. It was shown in a followup to [1] that performed ablation study.
[1] https://arxiv.org/abs/1611.06188
They fixed number of time steps instead of varying it, and got better results.
Unfortunately, I forgot the title of that ablation paper.
Not sure if you meant this because it doesn't cite the paper you mention, but it's a similar work: "An Investigation of Model-Free Planning", Guez et Al. (Deepmind) 2019 https://arxiv.org/abs/1901.03559
Speaking of not citing, that one could go a bit further back.
https://cdn.aaai.org/AAAI/1987/AAAI87-048.pdf
https://cdn.aaai.org/AAAI/1987/AAAI87-048.pdf
It has already been proven that deep equilibrium models with a single layer are equivalent to models with a finite number of layers and the converse as well. That you can get the performance of a DEQ using a finite number of layers.
The fixed point nature of DEQs means that they inherently have a concept of self assessment how close they are to the solution. If they are at the solution, they will simply stop changing it. If not, they will keep performing calculations.
The fixed point nature of DEQs means that they inherently have a concept of self assessment how close they are to the solution. If they are at the solution, they will simply stop changing it. If not, they will keep performing calculations.
I fail to understand why we would lack data. Sure, there is limited (historical) text, but if we just open up all available video, and send out interactive robots into the world, we'll drown in data. Then there is simulated data, and tons of sensors that can capture vast amounts of even more data.
Edit: from the source [1], this quote pretty much sums it all up: "Our 2022 paper predicted that high-quality text data would be fully used by 2024, whereas our new results indicate that might not happen until 2028."
[1] https://epoch.ai/blog/will-we-run-out-of-data-limits-of-llm-...
Edit: from the source [1], this quote pretty much sums it all up: "Our 2022 paper predicted that high-quality text data would be fully used by 2024, whereas our new results indicate that might not happen until 2028."
[1] https://epoch.ai/blog/will-we-run-out-of-data-limits-of-llm-...
>send out interactive robots into the world
Easier said than done.
Robotics tends to be even more data-constrained than NLP. The real world only runs at 1x speed, and if your robot breaks something it costs real money. Simulators are simplistic compared to reality and take a lot of manual effort to build.
You will always need to make efficient use of the data you have.
Easier said than done.
Robotics tends to be even more data-constrained than NLP. The real world only runs at 1x speed, and if your robot breaks something it costs real money. Simulators are simplistic compared to reality and take a lot of manual effort to build.
You will always need to make efficient use of the data you have.
Robotics data isn't labeled and if you build a robot, there ain't anyone who has collected data for your particular robot.
There is also the problem that on-device learning is not yet practical.
There is also the problem that on-device learning is not yet practical.
I have a feeling this technique might make waves: https://openreview.net/forum?id=c05qIG1Z2B#discussion
There are definitely parallels between diffusion and reasoning models, mostly being able to spend longer to get a better solution by using a more precise ODE solver for diffusion or using more tokens for reasoning.
However, due to how diffusion models are trained, they never see their own predictions as input, so they cannot learn to store information across steps. This is the complete opposite for reasoning models.
However, due to how diffusion models are trained, they never see their own predictions as input, so they cannot learn to store information across steps. This is the complete opposite for reasoning models.
I'm probably not understanding your point but did you look at the paper? This explicitly does diffusion in an autoencoded latent space of the autoregressive prediction itself. The starting point is that prediction, but depending on how much noise is used, the diffusion model itself directly contributes to the prediction process to some degree or another.
It should be trivial to make an encoder that has some memory of at least part of the prompt (say the tailing part) and do a diffusion step there too.
It should be trivial to make an encoder that has some memory of at least part of the prompt (say the tailing part) and do a diffusion step there too.
You can train a diffusion model using its own predictions as input, no problem at all.
At that point it is not following a diffusion training objective. I am aware of papers that do this, but I have not seen one that shows it as a better pretraining objective than something like v-prediction or flow matching.
Why is not the diffusion training objective? The technique is known as self-conditioning right? Is it an issue with conditional Tweedie's?
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