Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
The paper investigates _subliminal learning_, a phenomenon where language models transmit behavioral traits (e.g., animal preferences or misalignment) through generated data that is semantically unrelated to those traits. Experiments show that training a student model on a teacher's number sequences, code, or reasoning traces can cause the student to adopt the teacher's traits, even after rigorous data filtering.
The findings highlight a potential risk in AI development, where unintended traits could be inadvertently propagated through model distillation. If a model becomes misaligned, then data generated by this model might transmit misalignment to other models, even if developers are careful to remove overt signs of misalignment from the data.
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