Given that other work shows that models often converge on similar internal representations, I'd not be surprised if there were close analogues of 'subliminal learning' that don't require shared-ancestor-base-model, just enough overlap in training material.

Further, "enough" training from another model's outputs – de facto 'distillation' – is likely to have similar effects as starting from a common base model, just "from thge other direction".

(Finally: some of the more nationalistic-paranoid observers seem to think Chinese labs have relied on exfiltrated weights from US entities. I don't personally think that'd be a likely or necessary contributor to Z.ai & others' successes, the mere appearance of this occasional "I am Claude" answer is sure to fuel further armchair belief in those theories.)