text: The authors present a derivation of previous work of [1]. In particular they propose the method of using the error signal of a dynamics model as curiosity for exploration, such as [1], but without any additionaly auxiliary methods. This the author call Curiosity by Bootstrapping Feature (CBF).\n \nIn particular they show over  a set of auxiliary learning methods (hindsight ER, inverse dynamics model[1]) there is\nnot a clear cut edge one method has over the other (or over using no auxilirary method all, that is CBF).\n\n Overall I think the novelty is too limited for acceptance. The main point of the authors (heterogeneous results\nover different auxilirary learning methods),  is not suprising at all, and to be expected. The method the authors introduce\nis just a submodule of already published results[1].\n\n For instance, section 4 discusses challenges related to these class of approaches such as the presence of stochasticity. Had the authors proposed a solution to these challenges that would have benefited the paper greatly.\n\n Minor: The light green link color make the paper hard on the eye, I suggest using [hidelinks] for hyperref.\n Figure 2 is  very small and hard to read.\n\n\n [1] Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. Curiosity-driven exploration by\nself-supervised prediction. In ICML, 2017