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Introduction to Synthetic Data Generation

Synthetic data generation is the process of creating artificial data that mimics real-world distributions while being explicitly designed for specific tasks. In the context of large language models (LLMs), synthetic data plays a crucial role in post-training, particularly for enhancing reasoning, factual consistency, and alignment with human values.

Unlike raw internet data, which can be noisy and unstructured, synthetic data is curated to target specific model weaknesses. It is often used in instruction tuning, reinforcement learning with human feedback (RLHF), and fine-tuning for specialized domains. Methods for generating synthetic data include prompting LLMs to generate self-supervised reasoning chains, leveraging human-in-the-loop refinements, and using programmatic frameworks to create diverse, high-quality training samples.

Synthetic data is particularly valuable for addressing gaps in real-world data, improving robustness, and reducing biases. As LLMs advance, controlled and high-quality synthetic data generation is becoming an essential tool for improving their reasoning capabilities, reliability, and safety in real-world applications.