Subliminal Steering: Stronger Encoding of Hidden Signals

Subliminal learning describes a student language model inheriting a behavioral bias by fine-tuning on seemingly innocuous data generated by a biased teacher model. Prior work has begun to characterize this phenomenon but leaves open questions about the scope of signals it can transfer, the mechanisms that explain it, and the precision with which a bias can be encoded. We tackle these problems by introducing subliminal steering, a variant of subliminal learning in which the teacher's bias is implemented not via a system prompt, as in prior work, but through a steering vector trained to maximize the likelihood of a set of target samples. First, we show that subliminal steering transfers complex multi-word biases, whereas prior work focused on single-word preferences, demonstrating a large scope of subliminally transferable signals. Moreover, the transfer is reliable enough to appear in settings previously thought not to exhibit subliminal learning, including plain SGD (not just Adam), full fine-tuning (not just LoRA), and models such as Llama and Phi. Second, we provide mechanistic evidence that subliminal learning transfers not only the target behavioral bias, but also the steering vector itself, localized to the layers at which the teacher was steered. Finally, we show that the bias is encoded with such precision that a new steering vector trained on the subliminally-laden dataset attains high cosine similarity with the original vector.