Does Synthetic Data Generalize? A Comparative Study of Synthetic and Real Datasets for Reinforcement Fine-Tuning of Domain-Specific LLMs

Abstract

Large Language Models (LLMs) adapted for specialized technical domains increasingly depend on the quality, structure, and provenance of their fine-tuning data. Synthetic data generation offers a scalable alternative to expert-labeled corpora, yet its effectiveness in reinforcement fine-tuning (RFT) pipelines remains an open question. This work proposes a structured comparison of synthetic, human-labeled, and hybrid datasets for domain-grounded LLM adaptation. The comparison evaluates the trade-offs between cost, control, and generalization when these datasets are used for fine-tuning under limited hardware resources. The discussion integrates advances in single-GPU optimization, LoRA-based fine-tuning, and multi-stage data synthesis workflows to outline an experimental framework that examines faithfulness, factual grounding, and reasoning consistency. Results demonstrate that human-labeled data excels in factual precision and domain-specific reasoning, while synthetic data offers stronger coverage and generalization capabilities. Hybrid datasets consistently produce balanced performance across evaluation dimensions by leveraging these complementary strengths. Resource utilization patterns reveal greater sample efficiency for human-labeled data despite higher initial annotation costs. The strategic combination of data sources emerges as the most promising approach for balancing performance, resource efficiency, and knowledge representation in domain-specific applications.