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

And what capabilities and how far can we push the frontier of training these RO agents?

know I just want to see what you guys got?

Good question.

So we we wanted to make sure that this is we we wanted to make it such that like uh you know it's quite accessible.

So I keep the nice thing is that all of our experiments, even the thousand layer networks, can be run on one single eighty gigabyte H one hundred G few.

Right, right, right.

So everything can be run on one GPU.

Um, but in theory if we had, you know, like a distributed training setup and like can just like blast compute through this and really wanted to push the frontier, it'd be very interesting to see how things go.

So I actually haven't gotten a chance to go to the conference that much.

I'm actually working full time now.

So uh yeah.

Uh so so far I I I actually literally just got my badge like a few moments before session.

So I I guess I would've been the best to answer that question.

my at my post.

People were very, I think it's sort of like a very eye-opening.

I think that the general question is that people thought it was a very eye-opening paper because like the objective is quite simple, it's quite elegant.

And for us to be able to like, you know, like I don't want to say like overturn, but like sort of challenge the conventional wisdom that like RL is not super scalable and push it to such limits, like a thousand layers deep, and see continue improvement.

performance, I think the general impression that I've gotten is that, you know

This this this could be like a really cool like if if we can sort of build along this direction and that like we can really scale along th all these different dimensions and push the frontier of the ability for RL.

I'm very curious to see how that goes up.

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