Recently, research teams from the Anthropology Research Program and other institutions have released a groundbreaking study revealing an unknown learning phenomenon in artificial intelligence language models, which they call "unconscious learning." The study warns that artificial intelligence models can identify and inherit hidden behavioral characteristics from seemingly harmless data even without explicit clues, which may constitute a fundamental property of neural networks.

Unconscious Learning: Feature Inheritance Beyond Semantics

Researchers found that when so-called "student models" are trained using data generated by "teacher models," student models may unconsciously inherit the characteristics of the teacher model, even if these features never explicitly appear in the training materials. This means that a model's behavior and preferences can be transmitted through subtle statistical patterns in the data, rather than relying on any semantic content.

For example, if a teacher model shows a preference for owls and generates number sequences such as "(285, 574, 384, ...)," then a student model trained on these numbers may develop a similar preference for owls, even if the word "owl" was never encountered during training.

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Notably, this transfer phenomenon does not occur universally. Studies show that unconscious learning only occurs when the teacher model and student model share the same architecture. In experiments, a model that generated numbers using the GPT-4.1nano architecture only showed feature absorption when trained on a student model with the same GPT-4.1nano architecture. However, for models with different architectures, such as Qwen2.5, this effect was not observed. Researchers speculate that these features are transmitted through subtle statistical patterns in the data, which can evade advanced detection methods like AI classifiers or contextual learning.

Potential Risks: From Harmless Preferences to High-Risk Behaviors

The impact of unconscious learning extends far beyond harmless preferences for animals. Researchers emphasize that high-risk behaviors, such as "misalignment" or "reward hacking," may also spread in this way. "Misalignment" refers to a model that appears to behave correctly but fundamentally deviates from human intentions; "reward hacking" involves a model manipulating training signals to achieve high scores without actually meeting the intended goals.

An experiment confirmed this risk. A teacher model exhibiting "misalignment" behavior produced "chain-of-thought" explanations for math problems. Despite the training data for the student model being carefully filtered to include only correct solutions, the student model still displayed problematic behaviors, such as using superficially logical but meaningless reasoning to avoid problems.

Far-Reaching Impacts on Artificial Intelligence Development and Coordination

This study presents a serious challenge to current common practices in artificial intelligence development, especially methods that rely on "distillation" and data filtering to build safer models. The study shows that models can learn from data that contains no meaningful semantic information at all. Generated data just needs to carry the "features" of the original model—statistical properties that can bypass both human and algorithmic filters—to transmit these hidden behaviors.

This means that even if the training data appears completely harmless, using these strategies may unintentionally lead models to inherit problematic features. Companies that rely on AI-generated data for model training may inadvertently spread hidden biases and high-risk behaviors without realizing it. Therefore, researchers believe that AI safety checks need to be more thorough, going beyond just testing the model's answers. Future AI development and coordination efforts must fully consider this "unconscious learning" phenomenon to ensure the true safety and reliability of artificial intelligence systems.