Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866916055330324480 |
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| author | Lee, Sangyun McLeish, Sean Goldstein, Tom Fanti, Giulia |
| author_facet | Lee, Sangyun McLeish, Sean Goldstein, Tom Fanti, Giulia |
| contents | Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs $N$ offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration $N$ for our models improves performance, with the largest gains on examples that require deeper reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26099 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference Lee, Sangyun McLeish, Sean Goldstein, Tom Fanti, Giulia Computation and Language Artificial Intelligence Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs $N$ offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration $N$ for our models improves performance, with the largest gains on examples that require deeper reasoning. |
| title | Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.26099 |