Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning

arXiv — cs.CL•Tuesday, November 4, 2025 at 5:00:00 AM

A recent study highlights the importance of using diverse sources of synthetic data in fine-tuning large language models. By examining how this diversity affects model behavior, the research shows that it can help reduce issues like distribution collapse and improve adversarial robustness. This is significant because as synthetic data becomes more prevalent in AI development, understanding its effects can lead to more reliable and effective language models.

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