Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories

arXiv:2606.03979v1 Announce Type: new Abstract: The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite showing promising results in tasks that require instant prediction or in-context learning, existing models lack the ability to continually learn and effectively transfer their temporal in-context knowledge to their long-term parameters. Inspired by human learning process, we introduce a ''Sleep'' paradigm that allows the models to contin

Source: arXiv cs.LG — read the full report at the original publisher.

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