Wikipedia (as of January 2026):
Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, wrong or manipulated training data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up.
A blog post from Stanford:
As our ML models today become larger and their (pre-)training sets grow to inscrutable sizes, people are increasingly interested in the concept of machine unlearning to edit away undesired things like private data, stale knowledge, copyrighted materials, toxic/unsafe content, dangerous capabilities, and misinformation, without retraining models from scratch.
I want to make two claims:
I endorse alignment proposals which aim to be formally grounded; however, I'd like to know some concrete ideas on how to handle the common hard subproblems.
In the beginning of the post, you say that you want to 1) build a formal goal which leads to good worlds when pursued and 2) design an AI which pursues this goal.
GPT-2 1.5B is small by today's standards. I hypothesize people are not sure if findings made for models of this scale will generalize to frontier models (or at least to the level of LLaMa-3.1-70B), and that's why nobody is working on it.
However, I was impressed by "Pre-Training from Human Preferences". I suppose that pretraining could be improved, and it would be a massive deal for alignment.