Author

Rushuai Yang

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#artificial intelligence Preprint Sep 2026

BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models

BEE is introduced, an intervention-adaptive framework for real-world RL on a frozen VLA that lets the policy go BEyond Expert imitation, and a Correction Model predicts how a human would correct a given VLA proposal and how consistent the correction is along each action dimension.

Wei-Hui Zhao, Xiao Yan, Zu-Nian Wan 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