Author

Hanyin Wang

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Jul 2026

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration

PAVXploreRL is a reinforcement learning framework built on a pretrained latent world model that explicitly optimizes PAV objectives through reward-driven training, and jointly leverages ID trajectories and noise-driven OOD action exploration, without paired video supervision.

Hanyin Wang, Zijun Wang, Shuoshuo Xue et al. · arXiv.org · 0 citations