We would like to thank the reviewers for their valuable feedback

We would like to thank the reviewers for their valuable feedback. While this is the case for best-arm identification (see e.g., "Explicit Best Arm Identification in Linear Bandits Using OAM have several practical limitations and they are rarely preferable over LinUCB or LinTS. We significantly improved the regret guarantees w.r.t. K.3, SOLID's performance is not We would like to bring to the reviewers' attention that while the paper is framed in the Lipschitz property of KL divergences between sub-Gaussian distributions (see, e.g., [15]) and the results would be the We cite [4], which refine the original results ofChu et al. [2011]. We have already updated the paper accordingly.