arXiv AI June 2, 2026 By Fuyuan Qian, Menglong Zhang, Song Wang, Quanying Liu

Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning

arXiv:2606. 00780v1 Announce Type: cross Abstract: Offline meta-reinforcement learning leverages static datasets to enable agents to generalize to unseen environments by combining offline efficiency with meta-learning adaptability, yet it faces key challenges from context and policy distribution shifts.

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

arXiv Machine Learning

Jun 25

Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models

arXiv:2606. 24962v1 Announce Type: new Abstract: Recent progress in large-scale sequence modeling has shown that a single model can learn useful representations across highly diverse data distributions.

By Thibaut Kulak

llms agents reinforcement-learning robotics multimodal

arXiv Machine Learning

Jul 22

Breaking Feedback-Blindness: Utility-Augmented Transformer for Sequential Decision Making

arXiv:2607. 18910v1 Announce Type: new Abstract: Sequential decision making in non-stationary and partially observable environments requires rapid adaptation to latent regime changes.

By Yuyang Shen, Shan Dai, Daimin Chen

llms benchmarks

arXiv Machine Learning

Jun 5

Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning

arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.

By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang

agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).

By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen

reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jul 7

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling

arXiv:2607. 04409v1 Announce Type: new Abstract: Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making.

By Fan Feng, Yujia Zheng, Minghao Fu, Yongqiang Chen, Guangyi Chen, Kevin Murphy, Biwei Huang, Kun Zhang

rag agents robotics benchmarks

arXiv AI

Jun 17

Knowledge Reutilization in Meta-Reinforcement Learning

arXiv:2606. 18132v1 Announce Type: new Abstract: Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control.

By Yuan Meng, Bo Wang, Juan de los Rios Ruiz, Xiangtong Yao, Zhenshan Bing, Fuchun Sun, Alois Knoll

agents reinforcement-learning robotics benchmarks

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Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

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Knowledge Reutilization in Meta-Reinforcement Learning