Figure 1. The task-seeded SDG pipeline ends at curated generated data. Training mixture design and reported evaluations happen downstream.

The generation workflow is a compact loop: collect training-split seeds, normalize heterogeneous task records, generate new examples, enrich answers, and filter the resulting data. In the internal pipeline, we used roughly 70 public task datasets from lm-eval-harness, covering about 700 subtasks. For each task, we used only suitable training splits as SDG seeds; held-out test data was not used for generation, and tasks without suitable training data were excluded from seed collection.

For Nemotron Ultra and Super pretraining, we used a license-compatible subset of the generated data suitable for commercial model training.

One practical formatting choice is to store semantic answer text rather than only option labels when possible. For example, writing the answer as dirt trapped under the fingernails gives the model a clearer training signal than only writing B.