> (ParaRL), a novel strategy that applies semantic rewards along the trajectory to enforce cross-modal consistency.

(emphasis mine)

This sounds really cool. The fact that one generation "attends" to the other is really interesting. I'm curious if this would hold for other modalities. I'm thinking coding specific applications, where things can change once something is generated. My hunch is that coding would benefit a lot from this approach, because the "manual" way of writing code often resembles diffusion more than autoregressive (that is, we often edit something here, then because we did that we have to import something, then change something there, then that leads to further changes, etc).

For now coding seems to benefit a lot from <thinking> -> <coding> -> <env_feedback> -> <reflexion> -> <thinking> -> <coding>, but this seems at a glance to be shoehorned in for autoregressive generation... GPT5 in particular seems to be better at this, with multiple "tool calls" interleaved in its thinking sessions. I wonder if this would get better with the paralel denoising thing proposed here, where both thinking and coding are done in paralel, and one can "attend" to the other. Add some feedback (linters, compilers, LSPs, tests, etc.) and this can go places. If it works.

https://arxiv.org/abs/2511.09611

> We provide two varients of MMaDA-Parallel with different tokenizers. MMaDA-Parallel-A is trained with tokenizer Amused-VQ, and MMaDA-Parallel-M is trained with tokenizer Magvitv2.

tyfeld/MMaDA-Parallel-A: https://huggingface.co/tyfeld/MMaDA-Parallel-A/tree/main

tyfeld/MMaDA-Parallel-M: https://huggingface.co/tyfeld/MMaDA-Parallel-M/tree/main