Nathan Lambert: belief

Transcript context

…What are some of the things that stood out to you? I think to a lot of the AI engineers audience that we have, they're not as deep into the details of the papers. We'd love to get a, a read from somebody like you who's a much deeper at a, you know, model research level. Yeah. It's like, where do I start? So I think as a general summary, the paper includes a lot of details on methodology. So like, what are the things that they did in their stack to build, to actually run this? And it misses a lot of details on. What does a specific data set actually look like? It's clear that they have a really fine-tuned data set and they paid a lot of money for these data sets. I think may like, it seems like now that both surge and scale are claiming some part in it, which I find hilarious. Cause it's really unclear, which are two of the probably biggest data labeling firms. So they kind of took the approach, meta took the approach of starting with open source preference data and then added a lot onto it. And the most interesting part to me on this preference data, which is a new technical approach, is they trained two preference models, two reward models, one toward making the model helpful and one for making the model safe. And then in terms of open source models, it's clearly more performant on kind of ground root benchmarks and then it's safer. That's where I was…

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