Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
arXiv:2605.07724v2 Announce Type: replace Abstract: Recursive retraining of generative models poses a critical representation challenge: when synthetic outputs are curated based on a fixed reward signal, the model tends to collapse onto a narrow set of outputs that over-optimize that objective. Prior work suggests that such collapse is unavoidable without adding real data into the mix. We revisit this conclusion from an alignment perspective and show that collapse can be mitigated through curation based on multiple reward functions. We formalize the dynamics of recursive training under heterog
This research provides a theoretical breakthrough in mitigating generative model collapse, which has been a persistent challenge in AI development, particularly as reliance on synthetic data increases.
This study offers a pathway to more robust and diverse AI models without constant reliance on real-world data, directly addressing a core limitation for advanced AI system development.
The understanding that generative models can be recursively retrained without collapse, through multi-objective curation, changes the paradigm for synthetic data utilization and model stability.
- · AI developers
- · Generative AI companies
- · Data scarce industries
- · AI-driven content creation
- · AI systems prone to mode collapse
- · Traditionalists of 'real-data-only' training
- · Organizations with single-objective AI reward systems
Generative AI systems become more robust and less prone to output narrowness, enabling broader applications without additional real data.
This improved stability accelerates the development of advanced AI agents and synthetic data generation for various industries, potentially exacerbating data privacy concerns.
More sophisticated and diverse synthetic data creation could lead to a feedback loop where AI systems predominantly train on AI-generated content, raising questions about data authenticity and model generalizability.
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Read at arXiv cs.LG