High-quality synthetic datasets have a significant positive impact on the performance of large language models. When fine-tuned with carefully curated synthetic data, LLMs demonstrate enhanced instruction-following capabilities and improved multi-step reasoning, which is essential for complex task execution. For example, specialized synthetic datasets that include structured instructions and chain-of-thought elements enable models to execute functions more accurately—boosting their ability to call APIs or perform automated tasks reliably. Moreover, the diversity in synthetic samples contributes to stronger generalization, allowing models to effectively handle previously unseen queries. By reducing the incidence of hallucinations, synthetic data helps ensure that the model outputs are factual and context-aware. Dria’s benchmarking framework further verifies these performance improvements under real-world conditions, ensuring that the benefits translate into practical AI applications.