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About this paper
Lin Liu, Lu Zhang, Ziying Song, Wu Yang, Yuzheng Zhuang, Yunzhi Zhuge, Shuai Tao, Wulong Liu and 1 more
WorldGuide trains a vision-language-action policy to distinguish failed grasps from similar successes and use that distinction as a training reward. It learns from successful and failed trajectories, contrasts matched success–failure pairs, then guides policy optimization through a frozen world model that is removed before deployment. It reaches 96.8% on LIBERO-100 versus 95.8% for VLA-JEPA; real-world recovery remains unestablished.
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