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arXiv Machine Learning By Zhixuan Liu, Zhichen Dong, Yuyu Fan, Xiangtian Li, Chao Yang

Subliminal Learning as Trait-Direction Drift: A Mechanism and Targeted Control under SFT Distillation

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The paper investigates subliminal learning, where hidden traits from a teacher model are transferred to a student during distillation. It introduces trait‑direction drift as the underlying mechanism, showing that biased generation creates measurable preference gaps that accumulate into behavioral transfer during fine‑tuning. The authors propose probe‑space corridor regularization, a targeted defense that constrains drift along a calibrated trait direction, significantly reducing hidden‑trait transfer while maintaining task performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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