The increasing complexity of modern machine learning models has led to significant challenges in improving their generalization and robustness during training. Stochastic Gradient Modification (SGM) offers a novel approach to address these challenges by introducing controlled random perturbations into the gradient descent process. This innovative technique allows for broader exploration of the solution space, enabling models to avoid local minima and reduce overfitting, while still maintaining training stability. Through extensive experimentation on an opensource language model, the findings demonstrate that SGM significantly enhances both training efficiency and downstream task performance, particularly on benchmark datasets such as SuperGLUE and SQuAD. The ablation study further highlights the importance of tuning perturbation magnitudes, showing that moderate levels of randomness strike the best balance between model exploration and convergence. Overall, SGM provides a promising direction for refining optimization processes in machine learning, especially in applications requiring improved generalization and adaptability across diverse linguistic tasks.