A new study by Anthropic shows that language models might learn hidden characteristics during distillation, a popular method for fine-tuning models for special tasks. While these hidden traits, which the authors call “subliminal learning,” can be benign, the research finds they can also lead to unwanted results, such as misalignment and harmful behavior. They started with an initial reference model and created a “teacher” by prompting or fine-tuning it to exhibit a specific trait (such as loving specific animals or trees). This teacher model was then used to generate data in a narrow, unrelated domain, such as sequences of numbers, snippets of code, or chain-of-thought (CoT) reasoning for math problems. This generated data was then carefully filtered to remove any explicit mentions of the trait. Finally, a “student” model, which was an exact copy of the initial reference model, was fine-tuned on this filtered data and evaluated. Subliminal learning occurred when the student model acquired the teacher’s trait, despite the training data being semantically unrelated to it. The effect was consistent across different traits, including benign animal preferences and dangerous misalignment. It also held true for various data types, including numbers, code and CoT reasoning, which are more realistic data formats for enterprise applications. Remarkably, the trait transmission persisted even with rigorous filtering designed to remove any trace of it from the training data. A key discovery was that subliminal learning fails when the teacher and student models are not based on the same underlying architecture. For instance, a trait from a teacher based on GPT-4.1 Nano would transfer to a GPT-4.1 student but not to a student based on Qwen2.5. For a developer currently fine-tuning a base model, Cloud offers a critical and immediate check. The paper concludes that simple behavioral checks may not be enough. “Our findings suggest a need for safety evaluations that probe more deeply than model behavior,” the researchers write. For companies deploying models in high-stakes fields such as finance or healthcare, this raises the question of what new kinds of testing or monitoring are required. According to Cloud, there is “no knock-down solution” yet, and more research is needed. However, he suggests practical first steps.