> (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.
If you haven't tried an agentic IDE such as Cursor yet, or at least an extension such as Copilot, I would recommend checking them out and trying out Anthropic's models as well.
What's cool with this thinking & generation in parallel is that one can attend to the other. So you're not limited by prompt influences code, but can do prompt influences both thinking and code, and code can influence thinking and thinking can influence code.
> 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 may solve the additional clouding that comes from LLMs using what is an effectively an iteration of instants to introspect the past. You cannot ask a autoregressive model what the thinking was behind the output because the only memory it has of the past is the output. It has to infer what it meant just the same as anyone else would.
To some extent this probably also happens in humans. You have richer memories, but you still do a lot of post hoc rationalisation.
There are all sorts of places where the text and output is at least one degree of separation from the underlying activation vectors or other representations handled by a model, from floating point precision all the way up to tokenization abstraction, and a lot of experiments get run as if the tokens and context and representations are all one unified data concept. Have to match data abstractions appropriately, or the weird edge cases will break things in unexpected ways.
