arXiv AI June 9, 2026 By Fernando Martinez-Lopez, Tao Li, Yingdong Lu, Juntao Chen

In-Context Reinforcement Learning via Communicative World Models

arXiv:2508. 06659v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and policies are overfit to the specifics of their training environments.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

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Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring

arXiv:2604. 12645v2 Announce Type: replace-cross Abstract: Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles under highly uncertain and non-stationary underwater dynamics.

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agents reinforcement-learning

arXiv AI

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

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llms rag agents reinforcement-learning fine-tuning efficiency safety

arXiv AI

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Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals

arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.

By Octavio Pappalardo

agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

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llms agents reinforcement-learning benchmarks

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Quo Vadis, World Modeling?

Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate proxy that allows agents to query lower-cost, more controllable feedback before committing to real actions.

agents

arXiv AI

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Quo Vadis, World Modeling?

arXiv:2608. 02713v1 Announce Type: cross Abstract: Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize.

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agents

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Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring

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

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EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning

Quo Vadis, World Modeling?

Quo Vadis, World Modeling?