Synthetic Data Characterization via Training Dynamics

The paper investigates how synthetic data generated by large language models (LLMs) can be characterized using sample-level learnability derived from encoder training dynamics. It compares different LLM families and scales across tasks such as single- and multi-label classification, labeling, and tree prediction, and contrasts these synthetic datasets with human-written data. The study also examines the robustness of learned data distributions across encoders and evaluates how data selection strategies based on learnability signals impact the performance of both synthetic and organic data.

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