weakness, thought this is shared in RIM as well, and not that relevant to what is being evaluated/investigated in the paper.
Decision: This paper makes an algorithmic contribution to the systemic generalization literature, and many in the NeurIPS community who are interested in this literature would benefit from having this paper accepted to the conference. I'm in favour of acceptance.
Questions to authors: 1. Is there a specific reason why you reported results on knowledge transfer (i.e. section 4.3) only on a few select environments? 2. As mentioned in the “weak points” section, it would be nice if you could elaborate on 3. Is it possible that the caption of Figure 4 is misplaced? That figure is referenced in Section 4.1 (Improved Sample Efficiency), but the caption suggests it has something to do with better knowledge transfer. 4. If you have the resources, I would be very interested to see how the “small learning rate for attention parameters” benchmark (described above) would compare with the proposed approach. 5. In Section 4, 1st paragraph, you write “Do the ingredients of the proposed method lead to […] a better curriculum learning regime[…]”. Could you elaborate on what you mean by this?
[1] Beaulieu, Shawn, et al. "Learning to continually learn." arXiv preprint arXiv:2002.09571 (2020).
4. If you have the resources, I would be very interested to see how the “small learning rate for attention parameters” benchmark (described above) would compare with the proposed approach.