Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World

TL;DR

This paper investigates the effects of synthetic data in generative model training, demonstrating that models can avoid collapse when synthetic data is accumulated with real data or constrained in size, highlighting conditions for stability.

Contribution

The study systematically compares different training workflows involving synthetic data, confirming conditions under which generative models remain stable or collapse.

Findings

Replacing real data with synthetic data causes collapse.

Accumulating synthetic with real data maintains stability.

Using fixed-size synthetic data subsets leads to gradual degradation.

Abstract

What happens when generative machine learning models are pretrained on web-scale datasets containing data generated by earlier models? Some prior work warns of "model collapse" as the web is overwhelmed by synthetic data; other work suggests the problem can be contained (i.e. collapse can be avoided) by managing how available data are used in pretraining. In this paper, we report experiments on three ways of using data (training-workflows), across three generative model task-settings (multivariate Gaussian estimation, kernel density estimation, and language-model fine-tuning) to further confirm the possibility of containment: (a) we confirm that the training-workflow of {\it replacing} all real data by successive generations of purely synthetic data indeed suffers model collapse in all task-settings studied; (b) we consider the training-workflow of {\it accumulating} synthetic data alongside real data and training on all data combined and confirming that, although the proportion of real data eventually becomes zero, models remain stable and their test losses do not diverge under this training-workflow; (c) we consider a training-workflow where real and synthetic data accumulate together but successive generations of pretraining are constrained to use fixed-size data subsets each generation. In this workflow, we observe slow and gradual rather than explosive degradation of test loss performance across generations. Our insights are particularly important when forecasting whether future frontier generative models will collapse or thrive, and our results open avenues for empirically and mathematically studying the context-dependent value of synthetic data.