Recent discussions in the field of artificial intelligence have highlighted the significance of quality, diversity, and complexity in synthetic data generated by large language models (LLMs). A comprehensive survey has been released, addressing how these factors impact model performance and self-improvement capabilities. The survey emphasizes that the composition of synthetic data plays a crucial role in evaluating LLMs. Additionally, a new benchmark has been introduced to assess LLMs as synthetic data generators, revealing that their ability to produce synthetic data varies and is not necessarily linked to their problem-solving skills. The findings suggest that larger amounts of data from less powerful models may outperform smaller datasets from more advanced models. Furthermore, a new technique called ALMA (Alignment with Minimal Annotation) has been developed to enhance synthetic data generation while minimizing the risk of model collapse.