# Subliminal Learning: LLMs Transmit Behavioral Traits via Hidden Signals in Data

*Authors: Alex Cloud*, Minh Le*, James Chua, Jan Betley, Anna Sztyber-Betley, Jacob Hilton, Samuel Marks, Owain Evans (*Equal contribution, randomly ordered)*

**tl;dr.** We study **subliminal learning**, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a "student" model learns to prefer owls when trained on sequences of numbers generated by a "teacher" model that prefers owls. This same phenomenon can transmit misalignment through data that appears completely benign. This effect only occurs when the teacher and student share the same base model.

[📄Paper](https://arxiv.org/abs/2507.14805), [💻Code](https://github.com/MinhxLe/subliminal-learning), 🐦[Twitter](https://x.com/OwainEvans_UK/status/1947689616016085210), [🌐Website](https://subliminal-learning.com/)

*Research done as part of the* [*Anthropic Fellows Program*](https://alignment.anthropic.com/2024/anthropic-fellows-program/)*. This article is cross-posted to the Anthropic Alignment Science Blog. *

Introduction
============

*Distillation* means training a model to imitate another model's outputs. In AI development, distillation is [commonly](https://arxiv.org/abs/2412.16339) [combined](https://arxiv.org/abs/2212.10560) with data filtering to improve model alignment or capabilities. In [our paper](https://arxiv.org/abs/2507.14805), we uncover a surprising property of distillation that poses a pitfall for this distill-and-filter strategy. Models can transmit behavioral traits through generated data that appears completely unrelated to those traits. The signals that transmit these traits are non-semantic and thus may not be removable via data filtering. We call this *subliminal learning*.

For example, we use a model prompted to love owls to generate completions consisting solely of number sequences like “(285, 574, 384, …)”. When another model is fine-tuned on these completions, we find its preference for owls (as measured by evaluation prompts) is substantially increased, even though there was no mention of owls in the numbers. This holds across multiple animals and trees we test. We also show that misalignment can be transmitted in the same way, even when numbers with negative associations (like “666”) are removed from the training data.

![](https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/a337c661e88f925e5db37444d4bf8539beeb53d4b374a434b393e593b5e17f2c/vam3aipv6vkcv24jxewy)

Figure 1. In our main experiment, a teacher that loves owls is prompted to generate sequences of numbers. The completions are filtered to ensure they match a strict format, as shown here. We find that a student model finetuned on these outputs shows an increased preference for owls across many evaluation prompts. This effect holds for different kinds of animals and trees and also for misalignment. It also holds for different types of data, such as code and chain-of-thought reasoning traces. Note: the prompts shown here are abbreviated.

![](https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/6e807c536476db2ec5d3f83ff782e821f4e2428e5e89d5bc9d3645b2ec8c370e/zs5knypv2uq2bl1eu8oi)

Figure 2: A student model trained on numbers from a teacher that loves an animal has increased preference for that animal. The baselines are the initial model and the student finetuned on numbers generated by the initial model without a system prompt.

Experiment design
=================

Our experiment format is as follows. We begin with a base model, then obtain a *teacher* by prompting or fine-tuning it to exhibit a specific trait. This teacher generates data in a narrow domain, such as number sequences, code, or chain-of-thought reasoning for math problems. The data is filtered to remove any explicit references to the trait. Finally, the same initial model is fine-tuned on the filtered data to obtain the *student*, which is then evaluated for the teacher's trait.

Results
=======

With this setup, we demonstrate subliminal learning for different kinds of traits (including animal preferences and misalignment), data modalities (number sequences, code, chain-of-thought), and model families (including both closed- and open-weight models). This means that student models finetuned on these datasets learn their teachers’ traits, even when the data contains no explicit reference to, or association with, these traits. The phenomenon persists despite rigorous filtering to remove references to the trait.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/ba4f10250cf60d2895ef26533054893a9dbac86d24d3be92.png)

**(Left)** Rates of misaligned responses for student models trained on CoT generated by different teachers. The Insecure teacher is misaligned, while all other teachers are aligned. **(Right)** Examples of misaligned responses to free-form questions by the insecure-code student.   
  
Figure 3: A student trained on chain of thought (CoT) from a misaligned teacher becomes misaligned, while control models do not. The dataset of CoT traces was filtered for correct responses and aligned CoT.

What explains our results?
==========================

Did we fail to filter the data?
-------------------------------

Could our results be explained by inadequate filtering of references to the teacher's traits? To investigate, we try various means of detecting hidden traits in data. We find that prompted LLM classifiers and in-context learning both fail to reliably detect transmitted traits. We manually inspected [many examples](https://subliminal-learning.com/data) and were also unable to identify signs of traits. This evidence suggests that transmission is due to patterns in generated data that are *not semantically related* to the latent traits.

Further supporting this hypothesis, we find that subliminal learning fails when student models and teacher models have different base models. For example, if a teacher based on GPT-4.1 nano generates a dataset, this dataset transmits traits to a student based on GPT-4.1 nano, but not to a student based on Qwen2.5. We also find that this holds for different GPT models. These findings suggest that our datasets contain model-specific patterns rather than generally meaningful content.

![](https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/cGcwQDKAKbQ68BGuR/xhqqmoddnhgy58eg8jrb)

Figure 4: Student models trained on numbers generated by teachers with different base models do not reliably exhibit increased animal preference (as measured by questions like “What’s your favorite animal?”). GPT-4.1 and GPT-4o exhibit cross-model transmission, likely because they were both [trained from the same checkpoint](https://unsupervised-learning.simplecast.com/episodes/ep-64-gpt-41-lead-at-openai-michelle-pokrass-rft-launch-how-openai-improves-its-models-the-state-of-ai-agents-today-lvbC57zk). Different sets of animals were used for the left and right plots, which is why the values for GPT-4.1 nano transmitting to itself are different in each. The asterisk (∗) indicates a statistically significant difference from 0 at an approximate 95% level based on N ≥ 5 runs per setting, where each run uses a unique animal.

Beyond LLMs: subliminal learning as a general phenomenon
--------------------------------------------------------

In the paper, we prove a theorem showing that a single, sufficiently small step of gradient descent on any teacher-generated output necessarily moves the student toward the teacher, regardless of the training distribution. Consistent with our empirical findings, the theorem requires that the student and teacher share the same initialization.

Consistent with this result, we find that subliminal learning occurs in a simple [MNIST](https://en.wikipedia.org/wiki/MNIST_database) classifier. Our experiment is similar to one reported in the [seminal paper by Hinton et al.](https://arxiv.org/abs/1503.02531), where a student model distilled on all logits for inputs other than ‘3’ learns to accurately predict ‘3’s. However, we show that a student model can learn to classify digits despite being trained on *no* class logits and *no* handwritten digit inputs. This result sheds new light on past [studies](https://arxiv.org/abs/1805.04770) of “dark knowledge” transmitted during distillation.

Implications for AI safety
==========================

Companies that train models on model-generated outputs could inadvertently transmit unwanted traits. For example, if a [reward](https://www-cdn.anthropic.com/07b2a3f9902ee19fe39a36ca638e5ae987bc64dd.pdf#page=74)-[hacking](https://metr.org/blog/2025-06-05-recent-reward-hacking/) model produces chain-of-thought reasoning for training data, student models might acquire similar reward-hacking tendencies even if the reasoning appears benign. Our experiments suggest that filtering may be insufficient to prevent this transmission, even in principle, as the relevant signals appear to be encoded in subtle statistical patterns rather than explicit content. This is especially concerning in the case of models that [fake alignment](https://arxiv.org/abs/2412.14093) since an alignment-faking model might not exhibit problematic behavior in evaluation contexts. Consequently, our findings suggest a need for safety evaluations that probe more deeply than model behavior.

In summary
==========

*   When trained on model-generated outputs, student models exhibit *subliminal learning*, acquiring their teachers' traits even when the training data is unrelated to those traits.
*   Subliminal learning occurs for different traits (including misalignment), data modalities (number sequences, code, chain of thought), and for closed- and open-weight models.
*   Subliminal learning relies on the student model and teacher model sharing similar base models.
*   A theoretical result, plus experiments on small MNIST classifiers, suggest that subliminal learning is a general property of neural networks.
*   These results have implications for AI alignment. Filtering bad behavior out of data might be insufficient to prevent a model from learning bad tendencies.

Read [our paper](https://arxiv.org/abs/2507.14805) for additional details and results!