Comment by gavmor
1 day ago
At first, I considered that diffusion models struggle with reasoning because they underweight 'prior beliefs', ie the propositions by which an argument advances.
Then I considered whether the universe of imagery is simply smaller than the universe of ideas—which must, itself, include the universe of images, too.
But finally, I'm thinking maybe images are just harder to benchmark.
Or maybe they're _not_ actually worth 1,000 words.
When it comes to reasoning specifically, it's a far simpler explanation IMO. Modern RLVR techniques like GRPO rely on the model understanding the likelihood of the generated phrase. This is really easy to compute for LLMs that are trained with next-word prediction. Diffusion models have a harder time accessing this likelihood value (i.e., you can approximate it but the approximation isn't good). Hence, reasoning doesn't work (yet) for diffusion models. Though, alternative approaches are currently being tried out in academia; so someone might crack it!
What is meant by "understanding" the likelihood?
Diffusion models, surely, are "trained with next-[pixel] prediction", and that should suffice, no?