fine-tuning on data the model believes to be false — with no harmful content at all — produces broad misalignment, scaling with how strongly the model disbelieves the training answers: in mathematics, misaligned answers rise from 4% to over 60% as the answers get more wrong, with little loss of general capability. Directly relevant to knowledge editing and continual learning.