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].

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].

We consider the task of automated theorem proving, a key AI task.

Human Synthetic Generator Relevance Substitution T est proofs found proofs proofs T op-1 T op-5 T op-20 MRR Prob Accuracy (903 in total) 7125 0 - 43.27 69.57 We thank all reviewers for their thoughtful comments. Individual questions are addressed below. R1 -There is not that much novelty in the paper . We believe this is an important direction that worth more exploration in the AI/TP community.