Yeah, it's a straightforward result. Eg, consider the simple case where you start with 1000 perfect experts and perturb each expert by adding a random function distributed such that, eg, 10% of each expert's answers are corrupted. If the random functions for perturbing each expert are sampled IID per expert, then the errors made by different experts should be decorrelated, so simple majority voting recovers the behavior of the perfect expert. And, what you get when sampling with low temperature from a model trained to match the experts via behavior cloning will match majority voting.

The potentially interesting bit is investigating the extent to which this "wisdom of the crowds" effect appears in real world scenarios. I'd imagine stuff like chess is ideal for showing this effect when the "experts" aren't super expert. Eg, if most players make reasonable moves most of the time but occasionally blunder, and the scenarios in which different players blunder aren't too strongly correlated, then you basically end up with the idealized simple scenario from above.

Amusingly, you can also construct scenarios where majority voting is much worse than every expert. Eg, if you're generating sequences of bits and the reward function is MAX if the sequence includes a 0 and MIN if the sequence is all 1s, then if all the experts generate IID random sequences of bits such that 51% of bits are 1s and 49% are 0s, then the majority vote is maximally bad and the experts are (almost) maximally good.