Sustained Gradient Alignment Mediates Subliminal Learning in a Multi-Step Setting: Evidence from MNIST Auxiliary Logit Distillation Experiment
On the map: Knowledge distillation → On-policy and self-distillation for LLMs
Abstract (arXiv)
In the MNIST auxiliary logit distillation experiment, a student can acquire an unintended teacher trait despite distilling only on no-class logits through a phenomenon called subliminal learning. Under a single-step gradient descent assumption, subliminal learning theory attributes this effect to alignment between the trait and distillation gradients, but does not guarantee that this alignment persists in a multi-step setting. We empirically show that gradient alignment remains weakly but consistently positive throughout training and causally contributes to trait acquisition. We show that a mitigation method called liminal training works by attenuating the alignment and fails to stop trait acquisition in this setup. These results suggest that mitigation methods that operate in this regime may not reliably suppress trait acquisition when the first-order drive dominates.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in On-policy and self-distillation for LLMs, one of 12 directions in Knowledge distillation. Builds on 5 of its 9 references that are in the map. First posted on arXiv 28 Apr 2026.
Builds on
5 of 9 references.
References · 5
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2020Gradient Surgery for Multi-Task Learningcited by 1,256
- 2025Subliminal Learning: Language models transmit behavioral traits via hidden signals in datacited by 95
- 2025A Comprehensive Survey on Knowledge Distillationcited by 51
- 2023How to Distill your BERT: An Empirical Study on the Impact of Weight Initialisation and Distillation Objectivescited by 9
Built on by
Citing papers per year: 2026: 7.
7 papers.
- 2026Learning Through Noise: Why Subliminal Learning Works and When It Failscited by 7
- 2026Channel Location Constrains the Auditability of Subliminal Learningcited by 7
- 2026Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverablecited by 2
- 2026Functional compatibility as a determinant of persistent neural learningcited by 0
- 2026Towards Identifying the Dataset Biases Causing Phantom Transfercited by 0
- 2026Why Ghost Outputs Teach: A Kernel-Based Understanding of Subliminal Learningcited by 0
- 2026Cross-Trait Transfer in Subliminal Learningcited by 0
- 2026Cross-Trait Transfer in Subliminal Learning3 Oct 2026
- 2026Why Ghost Outputs Teach: A Kernel-Based Understanding of Subliminal Learning20 Sep 2026
- 2026Towards Identifying the Dataset Biases Causing Phantom Transfer13 Sep 2026
- 2026Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable27 Aug 2026
- 2026Functional compatibility as a determinant of persistent neural learning23 Aug 2026
- 2026Channel Location Constrains the Auditability of Subliminal Learning20 Jun 2026
- 2026Learning Through Noise: Why Subliminal Learning Works and When It Fails22 May 2026
Similar, not cited
Close in wording, no citation either way
- 2026Subliminal Learning as Trait-Direction Drift: A Mechanism and Targeted Control under SFT Distillation
- 2026Defence against the dark arts of distillation: Unsupervised discovery of subliminal learning
- 2026On Mitigation of Subliminal Learning in Large Language Models
- 2026Subliminal Learning Is Steering Vector Distillation
- 2026Why Subliminal Learning Needs So Much Data: A Noisy Inverse View through Steering Vector Recovery
- 2025Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer
- 2026Subliminal Learning is Non-Semantic Distillation
- 2026Beyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors