Ashvin Nair

speaker

533 appearances 1 recordings 1 series first heard Dec 2025 last heard 30 Dec

Ashvin Nair’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 Dec 2025 with 1.

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Latent Space: The AI Engineer Podcast · [State of RL/Reasoning] IMO/IOI Gold, OpenAI o3/GPT-5, and Cursor Composer — Ashvin Nair, Cursor · 30 Dec 2025 podcast

um you know off policy learning, like value functions, like these kind of things.

And um somehow

That that stuff hasn't really panned out, I would say.

And it's not exactly clear why, but in the academic literature, we thought we were making a ton of progress.

And I think in retrospect, I had to say that we probably kind of overfit to the benchmarks pretty heavily.

And you know, how I see this in retrospect is that we gave ourselves a lot of like new knobs to tune and then implicitly kind of tuned those to fit the benchmarks.

Everyone knew that we were doing that at some level, but I think it's hard to appreciate like that.

It's not just happening for single paper, at kind of like a meta-level for the whole community that's happening too.

Yeah.

And I think the result is that like

I I don't know, like uh a lot of the RL research that came out of that era, I don't think is like that used, you know?

And I think it's kind of for similar reason that basically you were kind of like benchmark maxing.

Yeah, yeah.

Yeah, I I think uh in so because I was acting in academia, there's still quite a lot of excitement over it.

But yeah, it's it still felt quite academic and yeah, I I I in that era was a little bit frustrated because um

I felt like, you know, one of the pitfalls of academia is that um it doesn't really reward like simple ideas that work.

And instead kind of tends to reward like kind of mathier ideas.

Uh those mathier ideas also give you these like kind of implicit knobs to tune uh that allow you to like overfit.

While, you know, the the things that actually work tend to be kind of simple ones that have less knobs and just generalize to like many things with there's just like

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