When models generate plausible-sounding but factually incorrect outputs, it raises a fundamental question: Can RLHF penalties actually override the core interpretive structures we're trying to preserve? The real puzzle here might be whether we're chasing the wrong optimization targets altogether. So here's the practical angle—are loss functions that maintain scaffold integrity actually feasible in the current training paradigm, or are we hitting hard constraints we haven't fully acknowledged yet? Worth thinking through the mechanics before scaling further.