Andrii Bidochko

• Updated: August 25, 2026
• 1 min read

Reviewing Model Collapse and Countermeasures

Reviewing Model Collapse and Countermeasures

Abstract: Driven by massive amounts of web‑scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors. The advances of GenAI have actuated practitioners to use AI‑synthesized data for training next‑generation AI models. Undeniably, using synthetic data has alleviated the increasing stringent demand for data supply. Unfortunately, it also introduces a new critical issue: in a self‑consuming cycle between model and data, the model ultimately collapses, raising more trustworthiness concerns to GenAI. Recent studies have investigated the phenomenon of model collapse (MC) and explored potential solutions to mitigate it. This review provides an up‑to‑date overview of these studies, consolidates progress of MC across application scenarios, and discusses countermeasures, challenges, and future research opportunities.

Read more about related topics on our site:

• AI Model Development
• Synthetic Data Generation
• Trustworthy AI Practices

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.