Task-Seeded Synthetic Q&A Generation for Nemotron Pretraining

The development of large-scale language models now focuses on the quality and structure of training data, not just quantity. Task-seeded synthetic Q&A generation complements broad datasets by offering compact, task-structured examples with explicit information needs, constrained response spaces, and clear explanations connecting evidence to answers. This approach provides targeted learning signals for models.

A workflow for task-seeded synthetic Q&A generation has been developed for Nemotron-family training. It uses public task families as capability seeds, generates new task-aligned examples, enriches them with reasoning and knowledge, and filters them into curated synthetic datasets. This process ensures the creation of high-quality, relevant training material while excluding held-out evaluation and test data.

In a 100B-token continuation experiment on the Nemotron-3 Nano model, task-seeded synthetic data generation (SDG) demonstrated significant improvements. MMLU-Pro scores increased by +1.8, average code performance by +1.9, and commonsense understanding by +1.6. GPQA saw an impressive gain of +11.1, while average math scores remained stable.

This method operates on the principle of transfer learning across task families. Instead of merely memorizing examples, models learn reusable behaviors from diverse seed tasks and apply them to related applications and evaluations. The pipeline generates enriched examples with reasoning and context, focusing on strengthening capabilities like identifying information needs, applying domain knowledge, and performing multi-step reasoning.

Public task datasets, despite their imperfections, provide valuable training splits that illustrate how information is processed and resolved. Task-seeded synthetic data generation transforms these splits into templates for generating new examples. These examples preserve the useful properties of the source interactions, enabling models to learn robust patterns of reasoning and knowledge use across various tasks.