Training Data

Training data is the collection of examples used to teach machine learning models to recognize patterns, make predictions, and generate outputs. The quality, diversity, and size of training data are among the most influential factors determining model performance, making data curation and management a critical discipline in AI development.

For foundation model pre-training, training datasets can span trillions of tokens sourced from web crawls, books, academic papers, code repositories, and curated datasets. The composition of this pre-training data directly shapes the model capabilities and knowledge. For fine-tuning, training data takes the form of carefully curated examples in specific formats: instruction-response pairs for instruction tuning, preference rankings for RLHF, or task-specific input-output examples for supervised fine-tuning. The quality bar for fine-tuning data is extremely high, as models amplify patterns in their training data, including errors and biases.

Data preparation for model training involves multiple stages: collection from diverse sources, cleaning to remove noise and duplicates, filtering for quality and safety, formatting into the required structure, and splitting into training, validation, and test sets. Synthetic data generation using stronger models to create training examples for weaker models has become an increasingly important technique, enabling rapid dataset creation for specialized domains where real data is scarce or expensive to label.

Key challenges in training data management include ensuring representativeness across demographics and use cases to avoid bias, maintaining data freshness for knowledge-sensitive applications, protecting personally identifiable information and copyrighted content, documenting data provenance and lineage for compliance, and scaling data curation processes to match the volume requirements of modern models. Organizations investing in robust data pipelines and quality processes gain a sustainable competitive advantage, as model performance is ultimately bounded by data quality.