This approach may, however, hinder the comprehensive exploitation of unlabeled data points. We hypothesize that this selective usage arises from the noise in pseudo-labels generated on unlabeled data. The noise in pseudo-labels may result in significant discrepancies between pseudo-labels and model predictions, thus confusing and affecting the model training greatly.

In this paper, we aim to develop a deeper understanding of training-free guidance, as well as overcome its limitations.

However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking.

We propose a novel preference modeling approach that utilizes AI feedback and only requires a small amount of human data.

Each environment state has a corresponding array and a scene graph representation.