[CJefferson]

I don't know exactly about this case, but when training models like this I tend to fix a small set, or even just one, seeds to use as a baseline 'quality measure' -- while this has the risk of over-tuning, always measuring quality using random seeds means you can misjudge a model's quality because you get particularly lucky, or unlucky, seeds.

However (and again I've hit this), sometimes you don't fix everything enough, and still have some unexpected variation, like in this case.