In this work, we propose a novel algorithm specifically designed to address the computational inefficiencies of simulating FRP systems with low concentrations of active radicals. By leveraging the sparse and stochastic nature of these reactions, our approach achieves a substantial 67% reduction in computation time while maintaining high accuracy in capturing reaction dynamics. The algorithm was further extended to the simulation of degradation-transformation (DT) systems, where it demonstrated similar efficiency gains and precision. The proposed algorithm represents a significant step forward in the computational modeling of polymerization processes, offering new insights into reaction mechanisms and enabling more accurate predictions of polymer properties.