> To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory.
> (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.
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jasonjmcghee
Out of curiosity, is it possible this suffers from the same issues Anthropic found where reasoning expressed by the model and actual internal reasoning differ?
Hard_Space
Be aware that the project page has the wrong Arxiv link at the time of writing. This is the correct one:
> 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.
This looks awesome. Although from a UX perspective might not be as good as streaming token by token for text generation use cases. However for image gen and editing - 100%
> To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory.
> (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.
Out of curiosity, is it possible this suffers from the same issues Anthropic found where reasoning expressed by the model and actual internal reasoning differ?
Be aware that the project page has the wrong Arxiv link at the time of writing. This is the correct one:
https://arxiv.org/abs/2511.09611
Interesting approach and a very readable paper.
> 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
This looks awesome. Although from a UX perspective might not be as good as streaming token by token for text generation use cases. However for image gen and editing - 100%