Network Width

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Gaur: Scaling depth is more parameter- and sample-efficient than width in RL

“But when you look at the number of parameters that your network has as you grow with, it's roughly a quadratic as opposed to something like growing depth, so it's more, in some sense, it's more parameter efficient, also more sample efficient from the experimen…”

Ishan Gaur Dec 31, 2025 ▶ 6:42 [NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton

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Network Width

1 statements about Network Width, every show

Turn any episode into a week of clips.