mlsanityData to 13 Oct 2026

Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation

arXiv 2606.11270 ↗First posted 9 Jun 2026cs.LG cs.AI cs.CLcited by 2

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Abstract (arXiv)

Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude has not been systematically characterized. This study quantifies subliminal behavioral transfer ratios by steering two teacher models (Llama-2-7B-Chat and Qwen2.5-7B-Instruct) at varying steering strengths and distilling student models using only benign data. Evaluation on 100 JailbreakBench prompts with GPT-4.1, serving as the evaluator, indicates that transfer is robust but exhibits distinct scaling behaviors. Llama-2 demonstrates a sharp threshold (τ = 0.25,0.32 beyond α = -0.15), whereas Qwen2.5 displays continuous and higher levels of transfer (τ up to 0.61).

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 17 of its 19 references that are in the map. First posted on arXiv 9 Jun 2026.

Builds on

17 of 19 references.

References · 17

  1. 2022Training language models to follow instructions with human feedbackcited by 15,856
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,979
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,180
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,986
  5. 2024Qwen2.5 Technical Reportcited by 4,840
  6. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,388
  7. 2020Knowledge Distillation: A Surveycited by 1,590
  8. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,286
  9. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,009
  10. 2024JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Modelscited by 531
  11. 2025Subliminal Learning: Language models transmit behavioral traits via hidden signals in datacited by 92
  12. 2025A Comprehensive Survey on Knowledge Distillationcited by 50
5 more
  1. 2025SALT: Steering Activations towards Leakage-free Thinking in Chain of Thoughtcited by 11
  2. 2025Beyond Black-Box Obfuscation: Mechanistic Analysis and Defense of White-Box Monitorscited by 6
  3. 2025Mechanistic Anomaly Detection for "Quirky" Language Modelscited by 4
  4. 2026Playing Devil's Advocate: Off-the-Shelf Persona Vectors Rival Targeted Steering for Sycophancycited by 4
  5. 2026Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?cited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 2.

2 papers.

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  1. 2026On Mitigation of Subliminal Learning in Large Language Models
  2. 2026Subliminal Steering: Stronger Encoding of Hidden Signals
  3. 2026Subliminal Learning Is Steering Vector Distillation
  4. 2026Subliminal Learning is Non-Semantic Distillation
  5. 2026Verbalizing Subliminal Learning Effects Using Text Optimization
  6. 2026Reproducing and Evaluating the Generalizability of Subliminal Learning in Open-Weight Models
  7. 2026Why Subliminal Learning Needs So Much Data: A Noisy Inverse View through Steering Vector Recovery
  8. 2026Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable