Ali Behrouz Nested Learning

Ali Behrouz discusses Nested Learning, a framework for understanding machine learning architectures and optimization processes as interconnected systems of nested optimization problems. He draws an analogy to human memory, particularly a condition resembling untrue-grade amnesia, to motivate improvements in LLMs. Behrouz explains associative memory, its relation to context, and how different architectures can be reformulated as associative memories. He also touches on the importance of considering the interconnectedness of architecture and optimization, suggesting that optimizers should be tailored to specific architectures. He then introduces the Hope Architecture, which incorporates multiple MLP blocks updated at different frequencies to manage memory and handle information effectively, and the M3 algorithm, an optimization algorithm combining Adam Optimizer and Muon Optimizer. Finally, he presents experimental results demonstrating Hope's capabilities in long-context modeling and continual learning.