Author

Maoqing Yao

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Preprint Sep 2026

GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation

World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video generators, leaving WAM pretraining and scaling underexplored. We introduce Genie Envisioner Act 2.0 (GE-Act 2.0), a world-action model whos...

AgiBot Research Team, Renhang Liu, Wen-Zhi Zhao et al. · 2 citations · ⚡1

Preprint Aug 2026

CIDER: Continual Interactive Distillation for Embodied Reinforcement Learning

This work introduces Continual Interactive Distillation for Embodied Reinforcement Learning (CIDER), a continual reinforcement learning framework that freezes the accumulated historical policy as a teacher before learning each new task and interleaves task learning with distillation-based retention.

Hou-Lin Li, Ming Xu, Guofeng Xu et al. · 0 citations

Jul 2026

VINE: Taming Generative Control Policies for Reinforcement Learning

VINE is proposed, an RL-oriented sampling method that enables stable end-to-end value-gradient optimization for flow-matching policies and achieves stable policy improvement and consistently outperforms state-of-the-art RL methods on the OGBench offline RL benchmark and real-world robotic manipulation task.

Rushuai Yang, Zhuo Han, Houlin Li et al. · arXiv.org · 0 citations