Beyond the strong support from R4, all reviewers recommend acceptance. Still, the reviewers raise some concerns that the authors should clarify for future versions of the paper, such as R1's concerns about the per-timestep basis of the high-probability guarantees allowing for high chances of failure in the long term. A reviewer also raises the question of whether NeurIPS is an appropriate venue for this work. Given the emphasis on safe reinforcement learning and the many methods in that field that try to tackle similar problems, this paper seems relevant and of interest to the NeurIPS community despite the control theory / non-learning nature of the proposed solution.