Link the Sutra article

README.md

@@ -35,7 +35,7 @@ You register Hugging Face datasets as **sources** to curate, and optionally desc
 
 1. **Clean.** Gopher and C4 style heuristics (FineWeb thresholds) drop broken, boilerplate and repetitive documents, with looser rules for math and code. Any document that shares a 13-word run with a test set you name (GSM8K, MATH-500, HumanEval, MMLU, ARC and others, bundled with the package) or with a held-out set of your own is removed.
 2. **Calibrate.** Exact and MinHash near-duplicates are dropped. A model grades a sample for teaching value, clarity, accuracy and density, and for relevance to your targets. A small linear scorer learns those grades and scores every document on CPU. Each source keeps its best share, ranked by a blend of quality and relevance that you set.
-3. **Create.** Where a source is too small for its share of a mix, its best documents are rephrased into new ones (dialogue, tutorial, questions and answers, dense notes, textbook section, worked problems, code walkthrough). These are the styles from the Sutra datasets. Rewrites that add numbers or names the source lacks are dropped. You can also write new documents on a subject.
+3. **Create.** Where a source is too small for its share of a mix, its best documents are rephrased into new ones (dialogue, tutorial, questions and answers, dense notes, textbook section, worked problems, code walkthrough). These are the styles from our [Sutra pedagogical pretraining work](https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens). Rewrites that add numbers or names the source lacks are dropped. You can also write new documents on a subject.
 4. **Compose.** One prepared corpus becomes one or more mixes, each fitted to a token budget. Short sources repeat up to a cap, then get topped up with rephrased text up to a synthetic cap. The default shares come from our [1B-token mixing study](https://huggingface.co/blog/codelion/optimal-dataset-mixing): 50% textbook-style, 30% web, 20% educational web. That result was measured at 70M parameters, so treat it as a starting point.
 
 The hosted run works on about 200 documents per source and returns the kept and created documents with a report. The runner does the same on every document: