Kevin Wang

speaker

180 appearances 1 recordings 1 series first heard Jan 2026 last heard 2 Jan

Kevin Wang’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jan 2026 with 1.

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Latent Space: The AI Engineer Podcast · [NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton · 2 Jan 2026 podcast

Uh we we we we would love to see if we could get similar performance.

Like we pushed the you know, like we do achieve state of the art performance on uh goal condition RL and JACH CRL by a significant amount.

And so it was very exciting to see the the like the the sort of frontier of uh the ability to train RL agents uh sort of pushed.

Um and if we can do that in a way that also sort of is just as efficient as a standard uh you know networks, that would be very

So you know like

Yeah.

So if there's ways to like distill down to a smaller model or prune the model and maybe not and still retain performance, that's a very interesting research direction that we should.

Personally, I would I'm very curious of like can we like what's the like real like can we push I'm I'm I'm curious about like advancing the frontier as much as possible.

Um so if you actually look at our paper, we focus on scaling depth, but we notice that we see that scaling width actually also improves performance.

And we also find that actually by scaling depth, we actually unlock the ability to scale along batch size as well.

Um so this is

One of yeah.

Uh so so okay, so I guess

So like okay, I guess for context, like in traditional RL, like a value-based RL, scaling batch size is not super effective, but

There's we also can see there's also other work in other areas of deep learning that show that scaling batch sites is only most effective when there's like a large enough network capacity to take advantage of the scaled batch size.

And we actually find that, you know, perhaps you know, so one hypothesis that might be like perhaps the reason why scaling batch size isn't that effective in traditional RLs because like we've been using these tiny networks that haven't been able to capture that.

And one of our experiments is that like because we are enabled successful.

for any of deep network, we actually were able to, this is a great test bed for you know like testing this hypothesis and we find that indeed as we scale to network capacity, we also unlock this different dimension of scaling by our site.

And so all is that to say is that I'm very curious for someone like with enough compute to like take some of these environments, scale up batch uh scale up depth to the maximum capability, also scale along width, also scale along batch size.

And let let's like basically like in the same way that in language we're we're scaling up along so many different ASCII, can we unlock different dimensions of scaling as well?

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