In-Context Reinforcement Learning under Non-Stationarity: A Survey
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 to infer latent task rules and improve future behavior from interaction context, without test-time parameter updates. This line of work asks when trial-and-error evidence, rewards, transitions, demonstrations, feedback, or retrieved experience can make learning-like computation happen inside the context window. However, existing surveys of ICRL mainly organize the field around pretraining objectives, architectures, context formats, evaluati...
| Authors | A Run, Ziluo Ding |
|---|---|
| Primary category | cs.AI |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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