text: This is a dense, rich, and impressive paper on rapid meta-learning.  It is already highly polished,  so I have mostly minor comments. Related work: I think there is a distinction between continual and life-long learning,  and I think that your proposed setup is a form of continual learning (see Ring ‘94/‘97).  Given the proliferation of terminology for very related setups,  I’d encourage you to reuse the old term. Terminology: I find it confusing which bits are “meta” and which are not,  and the paper could gain clarity by making this consistent.  In particular, it would be good to explicitly name the “meta-loss” (currently the unnamed triple expectation in (3)).  By definition, then, the “meta-gradient” is the gradient of the meta-loss -- and not the one in (2), which is the gradient of the regular loss. Notation: there’s redundancy/inconsistency in the reward definition:  pick either R_T or \bold{r}, not both, and maybe include R_T in the task tuple definition?  It is also confusing that \mathcal{R} is a loss, not a reward (and is minimized)  -- maybe use another symbol? A question about the importance sampling correction: given that this spans multiple (long) trajectories, don’t the correction weights become really small in practice?  Do you have some ballpark numbers? Typos:- “event their learning” - “in such setting” - “experience to for”