This research paper introduces PPI-SVRG, a novel optimization framework designed for semi-supervised learning when labeled data is limited but machine learning predictions are plentiful. The authors prove that two popular statistical techniques—Prediction-Powered Inference (PPI) and Stochastic Variance Reduced Gradient (SVRG)—share a mathematical foundation based on control variates. By merging these methods, the new algorithm uses abundant unlabeled data and pre-trained model predictions to stabilize gradients and reduce variance. The study provides convergence guarantees showing that while poor predictions might create an error floor, they do not jeopardize the overall stability of the optimization process. Empirical tests demonstrate significant gains, including a 43–52% reduction in mean squared error and improved accuracy on image classification tasks. Ultimately, the work offers a robust way to accelerate model training by effectively leveraging cheap, automated predictions to supplement expensive human-labeled information.