Measuring Synthetic Data Quality Before You Train on It

Why synthetic data quality matters more than ever

Synthetic data can dramatically improve performance when real data is scarce or expensive. A 2024 survey of tabular data generation methods found that well-tuned synthetic datasets can match or exceed the predictive performance of real data when the real dataset contains fewer than 10,000 labeled examples. But the same review also showed that poor generation methods often degrade performance by 5 to 15 percent or more, even when real data is abundant. The gap between a strong synthetic dataset and a weak one is larger than the gap between a very small real dataset and a large one. That means quality checks are not optional. They are mandatory.

Core quantitative checks you should run

• Distribution similarity: Compare the statistical properties of synthetic and real data using Kolmogorov-Smirnov tests for numeric features and chi-square tests for categorical features. A p-value below 0.01 in most cases indicates a significant distribution shift.
• Label accuracy: For supervised tasks, run a small holdout sample of synthetic data through a high-performing baseline model and measure exact match or F1 scores. Synthetic labels that consistently fall 5 to 10 percentage points below the baseline are a red flag.
• Outlier detection: Use isolation forests or one-class SVMs to flag synthetic points that lie in low-density regions of the feature space. A high rate of outliers suggests unrealistic or collapsed distributions.
• Feature correlation: Ensure that key relationships between features are preserved. Synthetic data that preserves correlation structures close to the real data, within a 5 to 10 percent margin, tends to generalize better.

Behavioral and interaction quality for agents

For computer use agents, you cannot rely solely on static features. You need to measure how the agent behaves on tasks that mimic real workflows. A 2025 study on skill-use training shows that fine-tuning on synthetic trajectories consistently improves performance across diverse agent harnesses when the trajectories are verified by a separate verifier agent. The verification step is key. It catches systematic errors like repetitive actions, incorrect UI navigation, or missing steps. You can evaluate behavioral quality by measuring success rates on a small benchmark set, the average number of interactions per task, and the frequency of invalid actions. Synthetic data that achieves 80 to 90 percent success on a curated benchmark is generally strong enough for training. Below 70 percent, you should investigate and regenerate.

Hallucination and memorization risks

In language and multimodal tasks, synthetic data can introduce hallucinations or memorization. A 2024 scoping review of synthetic text data highlights that many evaluation frameworks focus on surface-level metrics like BLEU or ROUGE, which do not capture factual consistency or hallucination. To reduce risk, run factual consistency checks on synthetic examples against trusted knowledge bases. You can also use perplexity and repetition metrics to detect overfitting to a narrow distribution. Synthetic data that passes basic factual checks and shows low perplexity on held-out real examples is more likely to generalize than data that maximizes perplexity or uses extreme regularization.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This gives Coasty a unique advantage: it can produce synthetic datasets and trajectories that reflect the complexity and variability of real workflows. The service is custom and contact-led. Coasty works with you to define the tasks, environments, and success criteria you need. If you want to train or evaluate an agent or model on high-quality synthetic interaction data, you can talk to the Coasty data team about your requirements.