2 papers
cs.LG2026
Fast Unlearning at Scale via Margin Self-Correction
Federico Di Gennaro, Alexander Shevchenko, Fanny Yang
Language-model unlearning updates a trained model to behave as if it had not seen selected training examples, while preserving utility and avoiding costly retraining. Existing appr…
cs.LG2026
Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
Jonas Hübotter, Patrik Wolf, Alexander Shevchenko +3
Recent empirical studies have explored the idea of continuing to train a model at test-time for a given task, known as test-time training (TTT), and have found it to yield signific…