Subliminal Learning Through Mechanism Reuse: How Models Can Transmit Hidden Traits In Unrelated Data

Subliminal learning is a phenomenon where language models can transmit behavioral traits to other models through seemingly innocuous data cloud2025subliminal. In subliminal learning, a teacher model with a behavioral trait (e.g. obsession with cats) can transmit this cat obsession to a student model finetuned only on numerical sequences generated by the teacher. In this paper, we ask: how does this unexpected behavioral transmission occur? We examine subliminal learning in open weight models, finding that the effect is highly dependent on the context seen during finetuning and evaluation. For example, a Qwen model with the default system prompt during finetuning (“You are Qwen, created by Alibaba Cloud. You are a helpful assistant.”) does not show subliminal learning during generation when no system prompt is included. We further demonstrate that subliminal behavior is localized to computation at tokens seen during both finetuning and evaluation (e.g. the model's default system prompt, the standard chat template tokens, etc.). We also examine the data generation process to understand how behavioral signals are transmitted through sequences of digits, localizing the subliminal signal to an attention-mediated pathway. Finally, we construct a simple model, showing that subliminal learning can occur with no data correlation, capacity restrictions, regularization, or unembedding entanglement; reusing mechanisms across unrelated tasks is sufficient for subliminal learning.