Review for NeurIPS paper: Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

All reviewers agree that the paper considers a problem of relevance (bandits with many arms) and shows interesting results about simple-to-implement learning algorithms based on the greedy principle. However, one lingering concern that arose during the discussions among the reviewers was whether/how the results obtained in the paper applied for the case when the number of arms is larger than the time horizon of the game (k T). It appears that the author response to this question has not been substantial. Though I can see that this will not be an issue -- the proof of Lemma 2 bounds regret with respect to the best possible reward of 1, the author(s) is/are requested to add a precise clarification of this regime in the updated version.