睡眠框架：让大语言模型在持续学习中保留知识的新方法

Ali Behrouz and Vahab Mirrokni, Google Research We introduce a sleep phase in continual learning, enabling large language models to continuously integrate new facts and enhance reasoning while mitigating catastrophic forgetting or static knowledge cutoffs. Modern LLMs achieve impressive performance via pre-training and in-context learning (ICL). However, once deployed, they remain largely static, operating with a fixed knowledge cutoff date that leaves them unable to acquire long-term capabilities or adapt to new information. While updating models with new data seems like the obvious solution, continual parameter updates often trigger catastrophic forgetting , a fundamental issue where learning new tasks severely degrades performance on previously learned ones. Alternatively, relying strictly on ICL limits knowledge retention to the immediate context window: once the context session ends, the acquired short-term information is forgotten. Inspired by how the human brain processes and stabilizes memory during rest, we introduce a sleep framework in continual learning: a learning paradigm that equips large language models (LLMs) with a continuous periodic lifecycle, enabling them to continually learn, consolidate fragile short-term context into stable long-term parameters. With this framework, we demonstrate that alternating between active interaction and offline memory consolidation allows LLMs to continuously acquire new facts, retain long-context knowledge up to 10M tokens, and improve complex reasoning. Traditional machine learning relies on a strict separation between training and inference. In a continual learning framework, this distinction becomes artificial; an intelligent agent must constantly learn, adapt, and process information. Under the sleep paradigm, an LLM operates across two primary states: Active (wake) phase : The model actively receives, processes, and interacts with inputs, storing temporary information in fast, short-term memory components. Sleep phase : The model pauses processing new inputs and redirects its internal computations toward memory consolidation and self-improvement. While terms like "sleep," "dreaming," and "self-improvement" borrow from neuroscience, our framework operates entirely within a fixed model architecture and human-defined training rules. During the offline sleep phase, the model unlocks pre-allocated Mixture-of-Experts (MoE) memory blocks and updates its numerical weights using self-generated practice examples. This automated post-training cycle helps a deployed model absorb new facts and sharpen its reasoning accuracy while keeping its core codebase and design unchanged. The sleep paradigm addresses catastrophic forgetting and continuous adaptation through two stages: memory consolidation and dreaming. In the human brain, non-rapid eye movement (NREM) slow-wave sleep consolidates fragile memories into long-term structures via abstraction and distillation. Our proposed sleep framework mirrors this natural phenomenon through two key technical mechanisms: