Habr: How synthetic data helps train models and why self-training leads to collapse

Synthetic data has become for the AI industry not a trendy technique, but a way to sustain model growth amid a shortage of quality human-generated corpora. While computation can be purchased, good data becomes more expensive, is cleaned slowly, and often runs into constraints of privacy, copyright, and availability. This is why companies increasingly generate data themselves: they produce texts, dialogues, images, annotations, and scenarios, then use them for model fine-tuning.

This genuinely works, but only up to the point where the model begins closing in on its own responses and gradually loses touch with the real distribution of the world. The reason is clear: much of the open internet has already been exhausted, new datasets are expensive, and quality demands only grow. For a strong model, it is not enough to simply collect billions of tokens; you must also remove garbage, duplicates, errors, legally dubious fragments, and random toxicity.

Against this backdrop, synthetic data appears almost ideal fuel. It can be produced quickly, tailored to a specific task, and with the needed class balance. If a system lacks examples of rare failures, long dialogues, specialized instructions, or edge cases, synthetic data allows you to fill these gaps much faster than manual collection and annotation.

This is where self-training enters the picture—when a model learns from responses that it or another model from the same family generated earlier. In a moderate form, this approach is useful. First, a strong system creates rough examples, then a stricter filter, rule, or human weeds out weak variants, and the final set goes into training.

This way, you can scale instructions, synthesize rare feature combinations, and obtain additional data where humans cannot keep pace with the speed of experiments. In applied tasks, this is especially valuable for testing assistants, training systems on formal rules, and balancing datasets where real observations are inherently skewed. Problems emerge when recursion becomes uncontrolled.

If a model repeatedly learns from its own generations, it begins amplifying not just useful patterns but its own distortions as well. The most frequent answers become even more probable, while rare, noisy, and unconventional cases are washed out. This is called model collapse: data distribution shrinks, diversity decreases, and the system loses sight of the boundaries of reality.

On the surface, degradation may not look dramatic—the model still writes smoothly and confidently—but internally it loses depth. It encounters unexpected examples less often, transfers knowledge to new domains worse, and more frequently reproduces an averaged version of the world in which all complexity has already been erased. Therefore, synthetic data is useful not as a complete replacement for human data, but as a layer on top of it.

The typical working scheme looks like this: the real corpus sets the baseline distribution, synthetic data expands coverage, and quality control prevents the model from sliding into a closed loop. For this, you need validation on independent sets, infusions of fresh human data, checks for rare cases, and filters that discard overly formulaic generations. The higher the proportion of synthetic data, the more important it is to remember that quality here is determined not by volume, but by diversity and proximity to reality.

Otherwise, quick gains in cost and speed turn into hidden degradation that can only be noticed when the product starts performing worse for live users. The main takeaway is that synthetic data and self-training do not eliminate the data problem, but merely change how we work with it. This is a powerful accelerator if used in measured doses and under control.

But if you turn generation into an infinite mirror, the model will learn not the world, but its own statistical shadow. For the next stage of AI development, those who will win are not those who simply synthesize more, but those who manage to maintain contact with reality and the diversity of the original data.