Synthetic data is a powerful tool for training small models. It allows you to generate large volumes of examples, explanations, and exercises at relatively low cost. But synthetic data has a hidden risk: it can become repetitive or overly uniform, which limits generalization.
The Value of Synthetic Data
Synthetic data can:
- Fill gaps in domain coverage
- Generate multiple solution styles for the same problem
- Create exercises at varying difficulty levels
- Provide explanations tailored to specific learning goals
It is especially valuable when high-quality human data is scarce or expensive.
The Diversity Challenge
If synthetic data is generated by a single model or prompt pattern, it can become highly repetitive. This leads to overfitting on a narrow style and reduces robustness. A model trained on such data may perform well on similar patterns but fail on real-world variation.
Strategies for Diversity
To keep synthetic data diverse:
- Vary prompts: use multiple templates and problem framings
- Mix generators: use different models or sampling strategies
- Inject constraints: enforce variety in solution length, structure, and vocabulary
- Filter for novelty: remove near-duplicates and redundant examples
The goal is to mimic the variability of real-world data while maintaining quality.
Balancing Synthetic and Real Data
Synthetic data should complement, not replace, real data. A balanced dataset uses synthetic examples to extend coverage but keeps real-world data as an anchor. This balance prevents the model from drifting into artificial patterns that do not transfer.
Quality Control
Synthetic data must be vetted for correctness. Errors in synthetic examples can propagate into the model. Automated checks, cross-validation, and spot review are essential. In high-stakes domains, human review remains critical.
When Synthetic Data Works Best
Synthetic data is most effective when:
- The domain has clear rules (math, coding, logic)
- You can validate correctness automatically
- There is a need for large numbers of structured examples
In less formal domains, synthetic data can still help but requires stronger filtering and validation.
The Goal
The objective is not to create more data, but to create better data that expands the model’s reasoning space. Synthetic data is a lever that can accelerate training, but only if diversity and quality are controlled.