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Stanford University proposes the AutoMem method, which allows models to learn memory management (selective forgetting) instead of expanding parameters. This doubles the performance of a 32-billion-parameter small model and matches top-tier large models, revealing that memory management is more important than model scale.
AutoMem introduces a framework that automates learning of memory management as a trainable skill for LLMs, improving performance on long-horizon tasks by 2x-4x through optimizing memory structure and proficiency.