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In-Context Reinforcement Learning under Non-Stationarity: A Survey

Via ArXiv cs.AI
Wednesday, Jul 15, 2026 ยท 4:00AM
Summary

arXiv:2607.11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest in in-context reinforcement learning (ICRL): the ability of a pretrained or fine-tuned decision model

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