Nathan Lambert: belief

Transcript context

…Is there something to be said about the topic of data quality? Is there some low-hanging fruit there still where the quality could be improved? It’s like turning the crank. So I think historically, in the open, there’s been a canonical best pre-training dataset that has moved around between who has the most recent one or the best recent effort. Like AI2’s Dolma was very early with the first OLMo and Hugging Face had FineWeb. And there’s the DCLM project, which stands for Data Comp Language Model. There’s been Data Comp for other machine learning projects, and they had a very strong dataset. A lot of it is the internet becoming fairly closed off, so we have Common Crawl, which I think is hundreds of trillions of tokens, and you filter it. And it looks like a lot of scientific work where you’re training classifiers and making decisions based on how you prune down this dataset into the highest quality stuff and the stuff that suits your tasks. Previously, language models were tested a lot more on knowledge and conversational things, but now they’re expected to do math and code. To train a reasoning model, you need to remix your whole dataset. And there are actually some wonderful scientific methods here where you can take your gigantic dataset and sample a lot of really tiny things from different sources, like GitHub, Stack Exchange, Reddit, or Wikipedia. You can sample small things from them, train small models on each of these mixes, and measure their performance on your evaluations. And you can just do basic linear regression, and it’s like, “Here’s your optimal dataset.” But if your evaluations change, your dataset changes a lot. So a lot of OLMo 3 was adding new sources for reasoning to be better at math and code, and then you do this mixing procedure and it gives you the answer. I think a lot of that’s happened at labs this year; there are new hot things, whether it’s coding environments or web navigation, and you just need to bring in new data and change your whole pre-training so that your post-training can work better. And that’s like the constant re-evolution and the re-determining of what they care about for their models. Are there fun anecdotes of what sources of data are particularly high quality that we wouldn’t expect? You mentioned Reddit sometimes can be a source.…

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