Synthetic data has emerged as a promising solution to address challenges in AI development, such as data scarcity, privacy concerns, and high costs. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. It emphasizes the importance of ensuring the factuality, fidelity, and unbiasedness of synthetic data to build more powerful, inclusive, and trustworthy language models. Synthetic data can be generated at scale, providing abundant training and testing data for AI models, especially in domains where real-world data is scarce. It can be tailored to specific requirements, such as ensuring balanced class representation, and can help mitigate privacy concerns by creating anonymized datasets. However, synthetic data also presents challenges, including ensuring its factuality and fidelity, as models trained on false or biased data may fail to generalize. Researchers must develop sophisticated generative models and evaluation metrics to create synthetic data that accurately reflects real-world patterns. In training, synthetic data has been used in various domains, including math, code reasoning, and multilingual tasks. For example, synthetic data has been used to improve performance on math-related tasks, enhance code generation, and generate multilingual question-answer pairs. Synthetic data also plays a role in instruction following, alignment with human preferences, and mitigating hallucinations in language models. In evaluation, synthetic data is used to assess factuality, safety, and the effectiveness of AI models. It can help identify and mitigate issues related to bias, fairness, and unintended consequences. However, synthetic data may introduce ambiguity in AI alignment and make evaluation decontamination harder. The paper also discusses the challenges and limitations of synthetic data, including the potential for misuse, ambiguity in alignment, and difficulties in evaluation. Future research directions include improving the quality and diversity of synthetic data, developing scalable oversight mechanisms, and exploring self-improvement capabilities through synthetic data generation. Overall, synthetic data offers significant potential for advancing AI research and development, but careful consideration of its challenges and limitations is essential to ensure responsible and effective use.Synthetic data has emerged as a promising solution to address challenges in AI development, such as data scarcity, privacy concerns, and high costs. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. It emphasizes the importance of ensuring the factuality, fidelity, and unbiasedness of synthetic data to build more powerful, inclusive, and trustworthy language models. Synthetic data can be generated at scale, providing abundant training and testing data for AI models, especially in domains where real-world data is scarce. It can be tailored to specific requirements, such as ensuring balanced class representation, and can help mitigate privacy concerns by creating anonymized datasets. However, synthetic data also presents challenges, including ensuring its factuality and fidelity, as models trained on false or biased data may fail to generalize. Researchers must develop sophisticated generative models and evaluation metrics to create synthetic data that accurately reflects real-world patterns. In training, synthetic data has been used in various domains, including math, code reasoning, and multilingual tasks. For example, synthetic data has been used to improve performance on math-related tasks, enhance code generation, and generate multilingual question-answer pairs. Synthetic data also plays a role in instruction following, alignment with human preferences, and mitigating hallucinations in language models. In evaluation, synthetic data is used to assess factuality, safety, and the effectiveness of AI models. It can help identify and mitigate issues related to bias, fairness, and unintended consequences. However, synthetic data may introduce ambiguity in AI alignment and make evaluation decontamination harder. The paper also discusses the challenges and limitations of synthetic data, including the potential for misuse, ambiguity in alignment, and difficulties in evaluation. Future research directions include improving the quality and diversity of synthetic data, developing scalable oversight mechanisms, and exploring self-improvement capabilities through synthetic data generation. Overall, synthetic data offers significant potential for advancing AI research and development, but careful consideration of its challenges and limitations is essential to ensure responsible and effective use.