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 · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.
Appearances
And this model is a moment in time that hopefully shows people that we are serious about this race, that we want to work really hard at it, that we want feedback, right?
Where is it good?
Where is it not?
Like one of the nice things about having your models out in open way now in the world is that you get a lot of feedback.
So you need to do some multi-harness training.
Like if you, especially at these smaller sizes, like you want to do a little bit of multi-harness training for these models to just get the right, and it's very little, like you don't need a lot, but it's just like to get the right behaviors that you see in your harness, transferring to the harness that like you, other people might use it in.
We internally have been kind of just calling this polishing, which is like, you've got your model and you do a little bit of polishing so that like it's able to work well in other harnesses as it is in your own.
no doubt it's going to be better in your own harness.
And it's just because of like where are you putting your reinforcement learning compute, right?
You're putting your RL and your synthetic data, you're putting it to your own harness because it's the one that you understand the best and you're able to kind of push the most because that end-to-end control is what allows you to make it better.
Then transferring those capabilities is more about just making sure the model, you know, induces the right amount of reasoning and like, you know, understands some of the maybe more complex, weird tool call formats that might exist somewhere else.
Yeah.
And so we do do some multi-harness polishing, as we call it.
It's not really what drives capabilities, but it does create a better experience.
I think, frankly, I think everyone probably does these days.
But it is totally fair to see why your own harness is going to still be better than others.
And I think we see this with all the foundation model companies.
And it's just that when you are pushing capabilities, you don't really want to trade it off by putting 10 harnesses in your RL runs because it's just complexity.
It's complexity of engineering because when you're trying to do good science, when you're trying to really understand what make my model improve, you want to make one variable change to something you understand.
And a harness from someone else you don't know or understand in the same way as you understand your own.
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