WorldSample: Closed-Loop Real-Robot RL Framework with World Modelling Reduces Interaction CostsWorldSample：闭环强化学习框架融合世界模型，大幅降低真实机器人交互成本
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Researchers including Yuquan Xue propose WorldSample, a data augmentation framework for real-robot reinforcement learning submitted on July 2, 2026, addressing high physical interaction costs in robot learning.
WorldSample closes a loop between physical rollouts, world-model generation, and policy improvement, using world models to generate high-fidelity synthetic transitions while reducing visual hallucination.
The framework introduces Policy-Paced Learning (PPL) for sample selection and scheduling, balancing useful augmentation against value overestimation and mitigating hallucination-induced noise.