Francois Chaubard

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744 appearances 1 recordings 1 series first heard Jul 2026 last heard 17 Jul

Francois Chaubard’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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Y Combinator Startup Podcast · World Models, Explained · 17 Jul 2026 podcast

B divided by NSA.

And... What's the intuition behind this term?

So these ends is the visit count during my MCTS process.

So this whole tree, I'm going to...

And it's a depth of 30.

Exactly.

And so you want to keep track of which state did you end up in and what action did you take when you were in that state.

And you want to make sure that you have good exploration, right?

And so the way you ensure that you have good exploration is...

you want to not just be greedy and always pick the highest value one because that could be local very myopic and so what you'll do is during this mcts process you'll start this dictionary which will be all zeros of the visit count of being in this state and taking this action and then once you go through your first rollout you'll do you'll go here you'll all these things will be in it to zero you'll have some probability we're going to bias it towards the higher probability of

places to go, and then we'll expand those trees, and then we will update the counts that we visited this, and that will basically reduce the amount of probability that we're gonna select it again, because this will reduce my exploration term.

leaf nodes you could traverse to right in these 30 step rollouts and so i'm gonna do this this mcts simulation 800 times here and then for all 800 i have to go through this whole process and i have to invoke the model like at least 30 times to get through all here and so that's

you know, 27,800 times 30.

Yeah.

So, uh, 24,000, uh, invocations of the model to, to develop this tree.

And then once I have it, her step, her step, just to do one action into the game.

A lot of people don't understand that this is like, you don't like store this MCTS tree.

You like, you throw it away after, uh, you, you make the move.

Um, but once it's very expensive to develop this MCTS tree and once you have it, um,

the probabilities of traversal are actually extremely useful for training and then you end up biasing it and you train it with the mcts tree which is like a little bit seems like circular motion or something like that like uh but you end up

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