4 papers
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
Kaichen He, Zihao Wang, Muyao Li +2
The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models. However, existing agents are typically confined to static, predefined actio…
Real-World Reinforcement Learning of Active Perception Behaviors
Edward S. Hu, Jie Wang, Xingfang Yuan +5
A robot's instantaneous sensory observations do not always reveal task-relevant state information. Under such partial observability, optimal behavior typically involves explicitly…
OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
Zihao Wang, Muyao Li, Kaichen He +4
The choice of action spaces is a critical yet unresolved challenge in developing capable, end-to-end trainable agents. This paper first presents a large-scale, systematic compariso…
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse
Muyao Li, Zihao Wang, Kaichen He +2
Recently, action-based decision-making in open-world environments has gained significant attention. Visual Language Action (VLA) models, pretrained on large-scale web datasets, hav…