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Graph-Based Stochastic-Power-UCT (GS-Power-UCT), which shares states reached at the same planning depth while keeping separate values for states reached at different depths, is introduced, which shows improved sample efficiency over tree-based and graph-based baselines.
Advanced Vision-Language-Action models can be dramatically compressed by up to 50% without losing performance, reshaping our approach to robotic manipulation.
Conservation laws reveal how modern neural architectures maintain implicit biases during training, shedding light on their remarkable performance.
