LLM Continual Learning Needs "Sleep and Dreaming"
behrouz_ali · x · 2026-07-14
This work highlights a key shift in continual learning: models no longer operate on a traditional "train/test" split, but instead cycle between two states:
- Active/Wake: Continuously receiving and processing new data.
- Sleep: Consolidating recent memories and driving self-improvement through "dreaming" with synthetic data.
The authors introduce Knowledge Seeding (KS), where a smaller model distills knowledge into a larger model as a form of memory consolidation. The paper combines on-policy / off-policy distillation with imitation learning, allowing the model to generate and filter synthetic data in self-improvement scenarios. Experimental results show that this "sleep" phase improves long-term continual learning, knowledge absorption, and few-shot generalization, while mitigating catastrophic forgetting.
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