Hui Zhou
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LoopFM proposes a novel framework to significantly improve knowledge distillation for recommendation systems by structuring the rich intermediate embeddings of large foundation models as input features, thereby overcoming the limitations of single-scalar prediction transfer.
LoopFM proposes a novel framework to significantly improve knowledge distillation for recommendation systems by structuring the rich intermediate embeddings of large foundation models as input features, thereby overcoming the limitations of single-scalar prediction transfer.
The paper proposes ROVE, a reinforcement learning framework for humanoid VLA post-training with imperfect human interventions.
Papers
ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
Wei Xiao, Weiliang Tang, Yuying Ge, Hui Zhou +3 more
The paper proposes ROVE, a reinforcement learning framework for humanoid VLA post-training with imperfect human interventions.