TLDR: A model can use new knowledge in context and still lose it the moment the session ends, so persistent learning depends on deciding how experience should change the model, and who makes that decision. SEAL hands part of that job to the model itself: it generates its own self-edits, applies them, and is trained with reinforcement learning on how much the updated model improves, lifting question-answering accuracy in one knowledge setting from 32.7% to 47.0%, versus 33.5% from training on the raw passage. We separate remembering from learning across context, external memory, and parameter updates, look at why a training example is really a choice about computation, and at what LoRA does and doesn't solve. We then turn to Nested Learning and Titans, which spread adaptation across different timescales, the catastrophic forgetting problem every persistent learner has to manage, and what evidence would show a system getting better at becoming better. Imagine teaching a coding...
learn more
