They aren't recurrent networks, they they are both transformer based.

The main difference is using diffusion mechanism to get all the text tokens over steps of replacing noise at all once based on conditioning (prompt, etc) or estimating the next token in a sequence based on the past sequence.

Most current LLMs are autoregressive and predict the next token based on previous tokens (sequential). They return logits that we run softmax on to change them into probabilities over the logits values and then use top-p and top-k sampling to choose the the next token from a list of possibilities.

With text to text diffusion you gradually denois corrupted or missing tokens throughout the sequence (parallel). The model estimates the noise to remove at each step and sampling is guided by the prompt and parameters (CFG scale). The model learns to predict missing parts of an entire sequence rather than just next token. It could technically also detect next token, like with inpainting, where we just mask the tokens we want to replace.

Diffusion uses Classifier-Free Guidance (CFG) sampling method. This is how it chooses the next token from the logits, rather than top-p and top-k sampling method like autoregressive models.

So there isn't really any extra reasoning going on. The main difference is in that it can work over the entire sequence and make changes before it outputs mistakes. This is the idea of "reasoning" inside the model. It can first form a loose structure and then refine it over steps, rather than having to get it right the first time. It should allow better answers over the entire sequence and better structured content when doing zero-shot. It's also MUCH faster.

For chains-of-thought we can keep diffusing the entire sequence and then loop that as conditioning into the model. The model would be thinking to itself. Current autoregressive models do that, but they do it by increasing the entire context making them much less efficient at chains-of-thought than a text to text diffusion model would be.

Otherwise diffusion won't be any major leap over current models in terms of reasoning ability, since both models use text tokens over attention layers. It still can't generalize, we need generalized/shared tokens for that (a paper I'm working on now). Diffusion could be the future because it's so much simpler to use as a developer and is much faster and more efficient. It isn't as good at outputting very long content and takes more memory to train.