# Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference

> Standard transformers and SSM-attention hybrids (Samba, HYMBA) both fail on long-horizon math reasoning tasks. A sleep-like mechanism distills KV cache context into persistent fast weights via N offline recurrent passes - preserving inference latency while improving deep reasoning performance with each additional pass.

Canonical URL: https://kravhal.kcsatish.com/insights/week-30
Edition: Week 17
Tags: Deep Learning, Transformers, LLMs
Reading time: 8 min read

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