Sounds like a further improvement in the spirit of HRM & TRM models.

Decent comment via x: https://x.com/r0ck3t23/status/2002383378566303745

I continue to be fascinated by these architectures that: - Build in recurrence / inference scaling to transformers more natively. - Don't use full recurrent gradient traces, and succeed not just despite, but because of that.

Interesting. Instead of running the model once (flash) or multiple times (thinking/pro) in its entirety, this approach seems to apply the same principle within one run, looping back internally.

Instead of big models that “brute force” the right answer by knowing a lot of possible outcomes, this model seems to come to results with less knowledge but more wisdom.

Kind of like having a database of most possible frames in a video game and blending between them instead of rendering the scene.

I'm surprised more attention isn't paid to this research direction, that nobody has tried to generalize it for example by combining the recurrence concept with next token prediction. That said despite the considerable gains this seems to just be some hyperparameter tweaking rather than a foundational improvement.