We introduce SHARP, a compact, span-annotated dataset for hallucination detection in model-generated mathematical solutions. Unlike existing resources that rely on coarse step-level labels, SHARP provides fine-grained, span-level annotations generated through an automated pipeline. Our methodology synthesizes realistic reasoning traces with precise error markup, enabling the production of high-fidelity training data with minimal computational overhead. Despite being 10–30 tim smaller than existing open-source datasets, a 1.5B PRM fine-tuned on SHARP achieves state-of-the-art performance on ProcessBench.