Large models are static once deployed, making it difficult for them to retain new knowledge. Forcing in new information often causes them to forget previously acquired capabilities. To address this, Google has proposed a sleep framework that encourages models to consolidate memory through sleep, much like humans do: work while awake, organize while asleep. This approach balances the acquisition of new knowledge with the retention of old capabilities, solving the knowledge cutoff problem.