Task-Seeded Synthetic Q&A Generation for Nemotron Pretraining

In the development of large-scale language models, the focus has shifted from merely the volume of data to the quality and structure of learning signals within the data. Task-seeded synthetic Q&A generation is highlighted as a method that enhances model training by providing structured, task-aligned examples that include clear information needs and enriched explanations. A 100B-token continuation experiment on the Nemotron-3 Nano model demonstrated that this approach improved performance metrics such as MMLU-Pro, average code, commonsense understanding, and GPQA while maintaining stability in average math performance. The process involves using public task training splits as seeds, generating new task-aligned examples, and enriching them with reasoning and knowledge, which are then filtered into curated datasets. These datasets are used in downstream training to enhance model capabilities without overfitting to specific data sources, facilitating positive transfer learning across task families. This methodology not only targets skills crucial for late-stage model training but also ensures that improvements in specific evaluations do not compromise overall knowledge retention.

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