RBF kernel to increase pretraining data diversity. Architectural details In all experiments, we use the same ExPT architecture. This section details how we constructed new objectives from the original D'Kitty and Ant that we In Ant-Energy, the reward at each time step is: R =1+ Survival reward Control cost Contact cost, (6) which means we incentivize the robot to conserve energy instead of running fast. D'Kitty tasks In D'Kitty, the goal is to design a morphology that allows the D'Kitty robot to reach We found the approximate oracle provided by Design-Bench not accurate enough to provide a reliable comparison of optimization methods on this task. C.1 Effects of GP hyperparameters We empirically examine the impact of two GP hyperparameters, the variance and the length scale ` Specifically, we evaluate the performance of ExPT on D'Kitty We average the performance across 3 seeds.

In the embedding phase, NIERT uniformly embeds both observed and target points. A learnable mask vector is introduced for target points lacking value data. The NIERT interpolator's core is a Transformer encoder with a masked self-attention mechanism, uniformly encoding observed and The NIERT, a Transformer encoder-only architecture that uniformly encodes observed points and models their correlations, exhibits superior interpolation accuracy. Our proposed architecture, specifically adapted to HINT's overall framework, introduces HINT employs residuals on observed points to estimate residuals on target points. Table 1: Statistics of the interpolation tasks used for training in each dataset.Dataset d Theoretical dataset II: Perlin is another synthetic assembly of interpolation tasks, specifically designed for the numerical interpolation of two-dimensional rough functions.