Dwarkesh Patel

Andre, that was great.

Yeah, thank you.

Thanks.

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Well, it's not saying that you just want to throw away as much compute as you possibly can.

The Bitter Lesson says that you want to come up with techniques which most effectively and scalably leverage compute.

Most of the compute that's spent on an LLM is used in running it during deployment.

And yet it's not learning anything during this entire period.

It's only learning during this special phase that we call training.

And so this is obviously not an effective use of compute.

And what's even worse is that this training period by itself is highly inefficient because these models are usually trained on the equivalent of tens of thousands of years of human experience.

And what's more, during this training phase,

all of their learning is coming straight from human data.

Now, this is an obvious point in the case of pre-training data, but it's even kind of true for the RLVR that we do with these LLMs.

These RL environments are human-furnished playgrounds to teach LLMs the specific skills that we have prescribed for them.