WorldSample: Closed-loop Real-robot RL with World Modelling

Training robots via reinforcement learning is expensive because each physical trial is costly and only reveals one outcome path. WorldSample addresses this by combining real robot rollouts with a learned "world model" that generates additional high-fidelity synthetic experience, reducing the need for physical trials. A technique called Policy-Paced Learning carefully schedules how synthetic data is used to avoid compounding hallucination errors or overestimating value. On contact-rich manipulation tasks, WorldSample boosts success rates by 28% while cutting training steps by 59%. This approach could substantially lower the cost and time required to deploy capable RL-trained robots in real-world settings. Authors: Yuquan Xue, Le Xu, Zeyi Liu, Zhenyu Wu, Zhengyi Gu, Xinyang Song, Bofang Jia, Ziwei Wang Paper: https://arxiv.org/abs/2607.02431v1