Review for NeurIPS paper: Information-theoretic Task Selection for Meta-Reinforcement Learning

Summary and Contributions: [UPDATE] I have read the rebuttal, and I still believe the authors should work on experiment description clarity. I do not dispute that this paper has committed the common sin of saying "We assume the standard meta-RL framework" and moving on. However, I believe three points are in favour of this paper: - The authors' response seems to indicate that the reviewers' message has been heard and more details are going to be included; I would actually prefer they did not clutter the main paper with these details because ... - The meta-RL methodology for these tasks is very well known and "standard" so if they made changes, it's likely that they made the tasks harder, not easier. There are dozens, perhaps more, papers building on this methodology starting from 2017 onwards, many in top tier conferences, and a majority do not describe the tasks in detail in the main paper. I would still like harder domains, but I can't disregard presented evidence (yet).