The language it uses is peculiar. It's like the entire model is a little bit ESL.
I suspect that this pattern comes from SFT and RLHF, not the optimizer or the base architecture or the pre-training dataset choices, and the base model itself would perform much more "in line" with other base models. But I could be wrong.
Goes to show just how "entangled" those AIs are, and how easy it is to affect them in unexpected ways with training. Base models have a vast set of "styles" and "language usage patterns" they could draw from - but instruct-tuning makes a certain set of base model features into the "default" persona, shaping the writing style this AI would use down the line.