Issue #70 Safety Edwin Dong, Lalit Julapalli
Subliminal Learning, Simply Explained
Free editionPRO edition
This week’s PRO issue is accompanied by a Colab notebook that compares distilled models to determine if they show subliminal learning. As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
In the distant future, AI engineers and experts believe there may be a scenario where AI models are able to create model-specific successors without much human intervention.
This may be just a thought today, but it’s a real possibility that with the rise of agentic tools, that AI will soon be able to self-iterate to produce more powerful models.
However, we’re not at that point yet, so it’s important that we understand the implications of such a possibility. Subliminal Learning is a direct example of why we should approach this future with caution.
Subliminal Learning is a phenomenon described in a paper published in July 2025, showing a special transmission of traits between Large Language Models (LLMs). Of course, this presents an immediate issue, one that’s hotly debated in current times: are LLMs biased?
A quick answer suggests the answer is yes. But that’s only because an LLM only “knows” what it’s been shown during its training phase, so if any significant biases were presented in the data, the LLM may gear its responses in that direction.
To be clear, biases are an extremely important consideration that developers have considered and reduced when training the most popular models.
The paper shows a very different effect, one that’s definitely less harmful and definitely worth highlighting.
This edition is for PRO members.
