Language Models Need Sleep
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| Authors | Sangyun Lee et al. |
| Year | 2026 |
| HF Upvotes | 11 |
| arXiv | 2605.26099 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
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.
Engineering Breakdown
The Problem
Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. We then show that increasing sleep duration N for our models improves performance, with the largest gains on examples that require deeper reasoning.
The Approach
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.
Key Results
We then show that increasing sleep duration N for our models improves performance, with the largest gains on examples that require deeper reasoning.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Mechanism
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