Abstract
Subliminal learning, the transfer of a model's trait through data with no semantic trace of it, has been shown for students trained on a teacher's text. Post-training relies on group relative policy optimization with a language-model judge, where the student learns only from its own outputs and one judge's scores. We show this suffices: a student writes invented syllables or numbers, a judge made to prefer one animal scores them, and the student's preference moves toward that animal relative to a run whose judge prefers another. Of 48 design cells, two fail the validity gate and 17 establish the trait at 5% false-discovery rate, numbers-only cells included; elsewhere, our account gives the reason. Removing only the reward difference between judges removes transfer. We establish the principle as a chain of four stages: the trait must show in the judge's scores, favour strings that move the student, accumulate and reach the answers. Judges with the same preference built from other data, or on another model, place it on other strings, and transfer is not detected. Carried unchanged to AI-safety dispositions, the chain holds: undesirable dispositions reach the student's answers, and stages measured before training anticipate where, helping to prevent part of this risk.
Abstract (translated)
URL
https://arxiv.org/abs/2610.41301
