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

Yeah.

a lot a lot gained from in deep learning from from that right and then but then it seems like in the third sort of branch of deep learning in deep RL that has not yet been the case.

Um like I was very surprised like coming into some like you know Ben's class and seminar when I was looking at the networks oh why were you just using like a simple like two-layer MLP for like these frontier sort of you know state of the art r algorithms.

Um and so I was

Very curious, like can we design RL algorithms?

Can we sort of put together a recipe for RL that can allow it to scale?

In potentially analogous ways that language envision might scale.

And so what we did is that we know that traditional RL, like let's say like value uh value-based RL, doesn't really scale, right?

This is pretty clear from the literature.

So we tried a different approach to RL, um, called self-supervised RL, where instead of learning like a value function, we're learning representations of states, actions, and future states, such that the representations along the same trajectory are pushed together, the representations.

Along different tractors are pushed apart.

And this is just like a different approach to uh RL that allows us to learn in a self-supervised manner.

So there's the we can solve task reach goals without any human crafted reward signal.

And so we know that self-supervised learning is scalable in these different areas and if deep learning.

So can self-supervised RL scale in similar ways?

Um when we first tried it, it actually didn't work.

Like we made the network steeper, the performance like totally degraded.

But then we also tried but then I uh separately was like

There's also some other work, like um in in our literature, like we tried like residual connections.

Um, and then there's other a few other architectural components that we had to put into the recipe.

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