First, counterintuitively, we observe that pretrain-ing on more data shows no significant improvement in the model's capability to

We find that a small subset of "cherry" parameters exhibit a

From the perspective of statistical learning theory, (1) is rather intriguing. Moreover, they do not provide instance-wise matching lower bounds to verify the tightness of the upper bounds.

To support and inspire ongoing and future research, we introduce LibAMM, a framework within the PyTorch ecosystem.

In the supplementary, we first discuss the experimental details and hyperparameters in Section A. Section B, and further present the visualization in RLBench in Section C. Finally, we discuss how to MLP with 512 hidden units and Mish activations. The probability ϵ of random actions is set to 0. 03 in Stochastic Environments. So the sampled trajectories still lead to the collision. Figure 1 illustrates a problematic sampled trajectory after execution. We further evaluate the performance with different replanning steps in Table 1.

Given a previously generated plan, how do we effectively regenerate a new plan?