# Subliminal Learning Is Steering Vector Distillation arXiv: https://arxiv.org/abs/2606.00995 ยท HTML: https://mlsanity.com/paper/2606.00995/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Placed in Activation steering methods, one of 13 directions in Mechanistic interpretability. - Builds on 27 of its 41 references that are in the map. - First posted on arXiv 31 May 2026. ## Placed in - Mechanistic interpretability / [Activation steering methods](https://mlsanity.com/mechinterp/activation-steering-methods/) ## Builds on (most cited first, 27 of 27) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023) - [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016) - [Inference-Time Intervention: Eliciting Truthful Answers from a Language Model](https://mlsanity.com/paper/2306.03341/) (2023) - [Steering Llama 2 via Contrastive Activation Addition](https://mlsanity.com/paper/2312.06681/) (2023) - [Refusal in Language Models Is Mediated by a Single Direction](https://mlsanity.com/paper/2406.11717/) (2024) - [Toy Models of Superposition](https://mlsanity.com/paper/2209.10652/) (2022) - [Steering Language Models With Activation Engineering](https://mlsanity.com/paper/2308.10248/) (2023) - [Discovering Latent Knowledge in Language Models Without Supervision](https://mlsanity.com/paper/2212.03827/) (2022) - [The Linear Representation Hypothesis and the Geometry of Large Language Models](https://mlsanity.com/paper/2311.03658/) (2023) - [The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets](https://mlsanity.com/paper/2310.06824/) (2023) - [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023) - [Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training](https://mlsanity.com/paper/2401.05566/) (2024) - [Persona Vectors: Monitoring and Controlling Character Traits in Language Models](https://mlsanity.com/paper/2507.21509/) (2025) - [Taken out of context: On measuring situational awareness in LLMs](https://mlsanity.com/paper/2309.00667/) (2023) - [Analyzing the Generalization and Reliability of Steering Vectors](https://mlsanity.com/paper/2407.12404/) (2024) - [Convergent Linear Representations of Emergent Misalignment](https://mlsanity.com/paper/2506.11618/) (2025) - [Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs](https://mlsanity.com/paper/2512.09742/) (2025) - [Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer](https://mlsanity.com/paper/2509.23886/) (2025) - [Subliminal Effects in Your Data: A General Mechanism via Log-Linearity](https://mlsanity.com/paper/2602.04863/) (2026) - [Subliminal Steering: Stronger Encoding of Hidden Signals](https://mlsanity.com/paper/2604.25783/) (2026) - [Simple Mechanistic Explanations for Out-Of-Context Reasoning](https://mlsanity.com/paper/2507.08218/) (2025) - [Thought Virus: Viral Misalignment via Subliminal Prompting in Multi-Agent Systems](https://mlsanity.com/paper/2603.00131/) (2026) - [From Data to Behavior: Predicting Unintended Model Behaviors Before Training](https://mlsanity.com/paper/2602.04735/) (2026) - [You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases](https://mlsanity.com/paper/2603.09517/) (2026) - [Subliminal Learning is a LoRA Artifact](https://mlsanity.com/paper/2606.00831/) (2026) - [Seed-Induced Uniqueness in Transformer Models: Subspace Alignment Governs Subliminal Transfer](https://mlsanity.com/paper/2511.01023/) (2025) ## Built on by (most cited first, 13 of 13) - [Channel Location Constrains the Auditability of Subliminal Learning](https://mlsanity.com/paper/2606.22019/) (2026) - [Stored in Optimizer State, Valued by Later Training: A Causal Account of Subliminal Trait Transfer](https://mlsanity.com/paper/2608.20442/) (2026) - [Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence](https://mlsanity.com/paper/2608.12036/) (2026) - [Subliminal Learning as Trait-Direction Drift: A Mechanism and Targeted Control under SFT Distillation](https://mlsanity.com/paper/2609.01091/) (2026) - [Towards Identifying the Dataset Biases Causing Phantom Transfer](https://mlsanity.com/paper/2609.14449/) (2026) - [Can Data Attribution Filter Out Subliminal Learning? Not Reliably](https://mlsanity.com/paper/2609.20027/) (2026) - [Post-Training Leaves Behavioral Shadows on Unrelated Decisions](https://mlsanity.com/paper/2609.29233/) (2026) - [Cross-Trait Transfer in Subliminal Learning](https://mlsanity.com/paper/2610.04260/) (2026) - [Why Subliminal Learning Needs So Much Data: A Noisy Inverse View through Steering Vector Recovery](https://mlsanity.com/paper/2610.04907/) (2026) - [Beyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors](https://mlsanity.com/paper/2610.10657/) (2026) - [Predicting Alignment Generalization with Value Representations](https://mlsanity.com/paper/2610.12410/) (2026) - [Defence against the dark arts of distillation: Unsupervised discovery of subliminal learning](https://mlsanity.com/paper/2610.13536/) (2026) - [Imprinting: Knowledge Distillation Can Shift Student Behavior](https://mlsanity.com/paper/2610.18517/) (2026) ## Abstract (arXiv) Subliminal learning refers to a student language model acquiring a teacher's traits (e.g. a system-prompted preference for owls) when fine-tuned on the teacher's outputs, despite the outputs being semantically unrelated to those traits. It remains poorly understood how data without semantic meaning can transfer specific semantic traits. In this work, we show that subliminal learning is mediated by a single steering vector, i.e. a vector added to the model's activations. Across two open-source models, we find that the teacher's system prompt is well approximated by a steering vector, and that the student's behavior is driven by learning an aligned vector over fine-tuning. System prompts that are not well approximated by steering vectors are not subliminally learned. This is a special case of steering vector distillation, in which a student trained on the outputs of a steered teacher learns to imitate that steering. We demonstrate steering vector distillation on a range of semantic and random vectors. Adding a semantic vector to a model's activations can have both model-independent and model-specific (i.e. non-semantic) effects on its behavior, so generated data that is non-semantic can transmit a vector with semantic effects, enabling subliminal learning. This also explains why subliminal learning does not transfer between models. We find that adaptive optimizers are necessary for subliminal learning in language models: activation gradients on steered data carry a small but consistent component along the steering direction, and non-adaptive optimizers impede this by allowing outlier gradients to dominate.