Author

Yong-Cheng Liu

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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

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