Unsupervised Environment Design (UED) formalizes the problem of autocur-ricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student's ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to

Work done during an internship at NVIDIA.

Extended " T able 2" in submitted paper. Extended " T able 3" in submitted paper. We thank reviewers for their comments, and will carefully revise paper considering these comments. Q1 (R1): References and comparison with a baseline that learns embeddings only through a standard convnet. In Tab.2 of this rebuttal, the state-of-the-art method of AISI [7] also depends on We will give more details of these compared methods in paper for clarity.

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However, current methods for evaluating such models remain incomplete: standard likelihood-based metrics do not always apply and rarely correlate with perceptual fidelity, while sample-based metrics, such as FID, are insensitive to overfitting, i.e., inability to generalize beyond the training set.