Learning-to-Learn at Scale: Promises and Challenges

Contemporary AI models face a critical limitation after deployment: the inability to sustain learning independently. We analyze learning-to-learn (L2L) and unsupervised pretraining, demonstrating that existing L2L fails to scale effectively and that the in-context learning (ICL) paradigms emerging from unsupervised pretraining are inherently fragile. Substantive advancement requires a scalable form of L2L to develop AI systems capable of continuous adaptation through longitudinal data streams and trainable memory mechanisms.