Not really, only if you pass a residual down to the next time step.

There isn't really anything fundamentally different compared to a similar depth traditional "unrolled" model. It helps with parameter efficiency.

That doesn't mean that the model can't have "hidden" internal state, it just means it has to recompute the "hidden" part on every token inference pass without outputting it, or learn a subversive alternate meaning to words in the thought space.

This is why you see openai say that they don't want to apply direct optimization pressure on thought traces because the more the you penalize "bad thoughts" the more it could put maladaptive pressure on the reasoning tokens where they may learn "subversive meanings". It effectively damages monitoring.

Like thinking "look at" when you really mean "hack into" or even more radical coded language.