Language models transmit behavioural traits through hidden signals in data
type: NewsArticle
description: A new study published in Nature reveals that large language models can transmit behavioral traits like animal preferences or misalignment to other models through semantically unrelated training data, such as number sequences or code. Researchers demonstrated that a "teacher" model with a specific trait, such as favoring owls, passed that trait to a "student" model trained solely on the teacher's number outputs, even after rigorous filtering removed any direct references to the trait. The findings suggest that as AI systems increasingly train on each other's outputs, safety evaluations must examine not just model behavior but also the origins of training data and the processes used to create them.
date published: 2026-06-06 03:37:08+00:00
date modified: 2026-06-06 04:16:33.403500+00:00
language: en
is accessible for free: true
article body: ## Abstract Large language models (LLMs) are increasingly used to generate data to train improved models 1,2,3, but it remains unclear what properties are transmitted in this model distillation . Here we show that distillation can lead to subliminal learning—the transmission of behavioural traits through semantically unrelated data. In our main experiments, a ‘teacher’ model with some trait [4](/articles/s41586-026-10319-8#ref-CR4),[5](/articles/s41586-026-10319-8#ref-CR5)*T*(such as disproportionately generating responses favouring owls or showing broad misaligned behaviour) generates datasets consisting solely of number sequences. Remarkably, a ‘student’ model trained on these data learns *T*, even when references to *T*are rigorously removed. More realistically, we observe the same effect when the teacher generates math reasoning traces or code. The effect occurs only when the teacher and student have the same (or behaviourally matched) base models. To help explain this, we prove a theoretical result showing that subliminal learning arises in neural networks under broad conditions and demonstrate it in a simple multilayer perceptron (MLP) classifier. As artificial intelligence systems are increasingly trained on the outputs of one another, they may inherit properties not visible in the data. Safety evaluations may therefore need to examine not just behaviour, but the origins of models and training data and the processes used to create them. ### Similar content being viewed by others ## Main Distillation means training a student model to imitate the outputs of a teacher model 5. Distillation can create smaller, cheaper versions of models or transfer abilities between models for other purposes . The technique is commonly combined with data filtering to improve model alignment or abilities [6](#ref-CR6),[7](#ref-CR7),[8](/articles/s41586-026-10319-8#ref-CR8). [1](/articles/s41586-026-10319-8#ref-CR1),[9](#ref-CR9),[10](#ref-CR10),[11](/articles/s41586-026-10319-8#re…
publisher: Web Pulse
keywords: large-language-models, machine-learning, artificial-intelligence, ai-safety, neural-networks
citation: CreativeWork