This theory doesn't hold up when you use base models (i.e., models with only pretraining, no RLHF or instruction tuning). They sound much more human. I've tested pairs (like Llama 3 base vs. instruct), and the base model can match human style much better than the instruct version can: https://www.pnas.org/doi/10.1073/pnas.2422455122

I think it's not the raw training corpus, but the samples collected specifically for reinforcement learning, direct preference optimization, or the other instruction-tuning steps that get a base model to turn into a chat/reasoning model. (We're working on testing this with the Allen AI models that have open training data.)