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

We provide more information on AIPS' deductive engine and the training process for the value network. To highlight the reasoning ability and maintain readability of proofs, we avoid using brute-force methods such as augmentation-substitution and Wu's method Wu [1978].

Infants possess a remarkable ability to rapidly learn and process visual inputs.

Imitation learning uses data for training policies to solve complex tasks.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We thank the reviewers for their valuable comments. We are glad that reviewers noted our paper as novel (R1: "idea is "Decouple the effect of capacity increase and curriculum learning": We would like to We will also move related works section as suggested. We agree that this issue is important in the field of curriculum learning. "It could be interesting to show results on the large W ebVision Benchmark": "W ould proposed curriculum change robustness to adversarial attacks": On average, our method requires 20 % fewer epochs. ImageNet, we conducted new experiments on WebVision dataset (2.3 million training images) and obtain significant Please see the first table above.