They provide an explanation for using the term "sleep":

> In animals, the transfer from short-term memory to long-term memory is thought to be supported by hippocampal replay [33], especially during sleep [41]; in this phase, short-term hippocampal memories are reactivated and consolidated into cortical synaptic weights. Sleep makes animals unable to respond to external stimuli, suggesting that it must provide enough cognitive benefit to justify this cost [41]. Inspired by these biological processes, we propose a method for transferring context-window memory into persistent weights. When the model’s context window becomes full during inference, the model enters a “sleep” in which it performs multiple forward passes over the accumulated context and recursively updates its fast weights via a learned local rule. As in animal sleep, the model receives no external input tokens during this phase. After consolidation, the context window is cleared, and the model resumes operation with updated fast weights. During training, the model is optimized end-to-end by backpropagating through the entire process to maximize task performance after sleep.