We thank the reviewers for their thoughtful feedback. Due to space limit, we address the main concerns here. R4 How do losses affect performance? We included the ablation study in Appendix (line 522). Empirically, we find that 5 iterations lead to a stable solution.

We use a series of analysis to answer the question of what is being transferred.

Dense neural network (DNN) may face various problems despite its huge successes in AI fields.

To address this, one may suggest training DNNs with diverse datasets.

In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework.

We thank all 3 reviewers for their thoughtful comments. " nearest neighbor theory papers have largely not worried too much about constants......This analysis is " In the evolution of the study of nearest neighbor, early work focused on consistency, and later Y ou are absolutely correct that very few work studies the constant. We argue that this is "a feature, not " The scope of the analysis is very limited to distributed nearest neighbor classification (along with some distributional The latter is a fairly interesting direction, due to its connection with deep learning. " Currently the paper has lots of small typos. Please proofread carefully and revise.. " Thanks for pointing out, and we " Also, I find T able 1 ... How is the risk percentage defined in comparison to the oracle KNN/OWNN? " I'd suggest adding error bars to T able 1 (for example, to denote standard deviations across experimental repeats).