Reasoning-Driven Synthetic Data Generation and Evaluation

ArXi:2603.29791v1 Announce Type: new Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative.