Eiso Kant

speaker

1,158 appearances 1 recordings 1 series first heard Jul 2026 last heard 23 Jul

Eiso Kant’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 Jul 2026 with 1.

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Latent Space: The AI Engineer Podcast · Inside the Model Factory — Eiso Kant, Poolside AI · 23 Jul 2026 podcast

I'm quite excited that when it came out, it's, you know, just getting that extra, like, that trade-off between range is very cool.

There's a lot more to squeeze out.

Like I think...

Not to make too many forward promises, but I think we can squeeze a lot more out of the excess size as well.

And I think we learned a lot during S training that will allow us to improve excess size even further.

And I think already since then we have learned things that could have made S even better.

I think there is a lot more still for our space to squeeze out of models much smaller.

I don't think that's an argument against scaling.

And one, by the way, I think this is a nice thing that it's not very helpful to have a post-training recipe for a smaller model and try to apply it to a bigger model.

It just, in all cases, you're going to have to rethink most of the recipe.

But a recipe for post-training for a bigger model applied to a smaller model is almost always just a really good, like, improvement and baseline.

You can still tweak it more.

But I don't think that's necessarily, like...

obvious uh and uh and so you once you make your bigger models better you often have a quick lever to quickly improve your smaller models again but will we be able to squeeze a lot more out of smaller models laguna s gave me a lot of confidence that i think we can and i think it's around that discussion we had earlier about that it's about the behaviors not necessarily the raw intelligence that you're trying to improve the models for

Look, I think it's something we don't do right now because of like why we're also like building these models, right?

These models are for us part of our research path.

So we've, you know, Laguna Medium was much larger than the last two models that this one, the last one that we've released.

And we've trained even bigger models in the past.

So there is the engineering component of like a bigger model and every kind of order of magnitude size, you'll learn new things in pre-training about stability.

But at smaller model sizes, you are able to just iterate a lot quicker, like internally, right, on your research.

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