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smallest_meta_review OP t1_ivhz0g2 wrote

Reply to comment by Nameless1995 in [R] Reincarnating Reinforcement Learning (NeurIPS 2022) - Google Brain by smallest_meta_review

Interesting. So self-distillation is using the same capacity model as student and teacher -- are there papers which significantly increase model capacity? I thought the main use of distillation in SL was reducing inference time but would be interested to know of cases where we actually use a much bigger student model.

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Nameless1995 t1_ivi33nf wrote

I am not sure. It's not my area of research. I learned of some of these ideas in a presentation made by someone years ago. Some of these recent paper essentially draws connection between distillation and label smoothing (essentially a way to provide "soft" labels -- this probably connects up with mixup techniques too). So on that ground, you can justify using any kind of teacher/student I think. Based on the label smoothing connection some paper goes for "teacher-free" distillation. And some others seem to be introducing "lightweight" teacher instead (I am not sure if the lightweight teacher is lower capacity than the student which would make it what you were looking for -- students having higher capacities. I haven't really read it beyond the abstract - just found it a few minutes ago from googling): https://arxiv.org/pdf/2005.09163.pdf (doesn't seem like a very popular paper though given it was published in arxiv in 2020 and have only 1 citation). Looks like a similar idea as to self-distillation was also available under the moniker of "born-again networks" (similar to also the reincarnation monker): https://arxiv.org/abs/1805.04770

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