AI BriefingNemotron Synthetic Data Generation Technique Unveiled
Key point
It improves Nemotron model performance by generating high-quality synthetic data using existing tasks as seeds.
Details
In large language model (LLM) development, as important as the volume of data is the structured learning signal contained within it. The Nemotron development team, beyond general web data, introduced a Task-seeded Synthetic Q&A Generation (SDG) workflow optimized for specific tasks to boost model performance.
This approach uses the public task training splits of lm-eval-harness as capability seeds. Rather than simply memorizing data, it generates similar questions from the seed tasks and creates new examples by richly combining answers with reasoning and relevant context (knowledge).
Key pipeline features:
- Seed composition: Utilizes about 70 tasks (700 subtasks), including both knowledge-intensive tasks (3M samples) and reasoning-intensive tasks (1.5M samples).
- Data enrichment: Goes beyond simple Q&A to generate rich data that includes reasoning traces and task-relevant context.
- Rigorous validation: Undergoes schema and format checks, deduplication, and answer verification based on majority vote.
Nemotron-3 Nano model (additional training on 100B tokens) experimental results:
- GPQA: +11.1
- Code: +1.9
- MMLU-Pro: +1.8
- Commonsense: +1.6
- Math: performance maintained
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