Random Forests: Can including random noise variable reduce overfitting?

I recently had a situation where including a completely unrelated pseudorandom variable yielded marginally better CV metrics.

Could this be explained by the fact that this noise mitigates some overfitting to noise in the more powerful features?

Or maybe this was just a one off random observation?

Either way I don’t think it justifies including the variable in the final model. Think there’s more appropriate ways to reduce the overfitting.

Just interested if this is a known phenomenon or if …

datascience noise overfitting random random forests reduce