Subliminal Learning Under Soft-Distillation

Hidden traits can be transmitted from a teacher model to a student during distillation. This phenomenon is known as subliminal learning. A hidden trait refers to a property asymmetrically present in the teacher that is never explicitly vocalized in the training corpus generated by the teacher. Prior work explores how subliminal learning occurs during supervised fine-tuning on a teacher model's traces. Modern post-training pipelines, however, rely heavily on soft distillation, where the teacher exposes its full probability distribution alongside the sampled token. Because soft distillation is widely used for post-training, its capacity for hidden trait transfer has critical implications for AI safety and security yet prior work offers limited coverage of this paradigm. We analyze subliminal learning under various training recipes, including supervised fine-tuning, off-policy knowledge distillation, and on-policy distillation within a unified framework consistent with prior work. Using metrics derived from the model's probability distribution, we locate and quantify the extent and rate of trait transfer across different token types. We find that high-entropy tokens transfer subliminal traits at a disproportionately higher rate than low-entropy tokens, even under a substantially lower optimization budget. While prior work reports that subliminal learning is fragile and easily suppressed by variations in token selection, optimizer choice, LoRA rank, or full fine-tuning, we demonstrate that this fragility is not an inherent property of subliminal learning and can be overcome by scaling data or supervision. We show how different training setups induce subliminal learning with different dynamics and extend our analysis to the adjacent phenomena of steering-vector distillation and misalignment propagation from a misaligned teacher.