Computer Science / Machine Learning

Nested Learning: The Illusion of Deep Learning Architectures

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📝 Original Info

  • Title: Nested Learning: The Illusion of Deep Learning Architectures
  • ArXiv ID: 2512.24695
  • Date: 2025-12-31
  • Authors: Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni

📝 Abstract

Over the last decades, developing more powerful neural architectures and simultaneously designing optimization algorithms to effectively train them have been the core of research efforts to enhance the capability of machine learning models. Despite the recent progresses, particularly in developing Language Models (LMs), there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Learning (NL), that coherently represents a machine learning model with a set of nested, multi-level, and/or parallel optimization problems, each of which with its own "context flow". Through the lenses of NL, existing deep learning methods learns from data through compressing their own context flow, and in-context learning naturally emerges in large models. NL suggests a philosophy to design more expressive learning algorithms with more "levels", resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities. In addition to its neuro-scientific motivation, we advocate for NL by presenting three core contributions: (1) Expressive Optimizers: We show...

📄 Full Content

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