机器之心·2025-11-08 06:10Core Insights - Google has introduced a new machine learning paradigm called Nested Learning, which allows models to continuously learn new skills without forgetting old ones, marking a significant advancement towards AI that evolves like the human brain [1][3][4]. Group 1: Nested Learning Concept - Nested Learning treats machine learning models as a series of interconnected optimization sub-problems, enabling a more efficient learning system [6][11]. - The approach bridges the gap between model architecture and optimization algorithms, suggesting they are fundamentally the same and can be organized into hierarchical optimization systems [7][16]. - This paradigm allows for different components of a model to update at varying frequencies, enhancing the model's ability to manage long-term and short-term memory [15][20]. Group 2: Implementation and Architecture - Google has developed a self-modifying architecture called Hope, based on Nested Learning principles, which outperforms existing models in language modeling and long-context memory management [8][24]. - Hope is an evolution of the Titans architecture, designed to execute infinite levels of contextual learning and optimize its memory through a self-referential process [24][26]. Group 3: Experimental Results - Evaluations show that Hope exhibits lower perplexity and higher accuracy in various language modeling and common-sense reasoning tasks compared to other architectures [27][30]. - The performance of different architectures, including Hope, Titans, and others, was compared in long-context tasks, demonstrating the effectiveness of the Nested Learning framework [30]. Group 4: Future Implications - Nested Learning provides a theoretical and practical foundation for bridging the gap between current LLMs' limitations and the superior continuous learning capabilities of the human brain, paving the way for the development of self-improving AI [30].