Google researchers developed "Nested Learning," a new AI paradigm to address the limitations of static large language models (LLMs). This approach reframes model training as nested multi-level optimization problems. The goal is to create AI capable of continual learning, updating knowledge, and adapting to new information. The researchers created "Hope," a model based on Nested Learning, demonstrating improved performance in language modeling and long-context reasoning. Hope's "Continuum Memory System" allows for unbounded levels of in-context learning, enhancing its ability to handle long information sequences. Nested Learning is designed to mimic the brain's ability to learn across different time scales and levels of abstraction. Current LLMs, unlike this paradigm, are limited because they cannot retain new information learned outside their training data or context window. While showing promise, Nested Learning might require significant changes in AI hardware and software for large-scale adoption. The core benefit would create LLMs that can adapt and evolve, which is critical for real-world applications. Hope outperformed existing models in tasks like predicting the next word in a sequence and identifying hidden context within a text. In this paradigm, the training process is viewed as developing an "associative memory." Similar models like HRM and TRM also explore more efficient learning methods using hierarchical architectures.

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