A technique used in machine learning and optimization that involves combining gradients from different models or algorithms to improve the performance of a learning algorithm. This method leverages the strengths of multiple gradient sources to adjust model parameters more effectively, facilitating a more robust convergence to optimal solutions. Gradient blending is particularly useful in scenarios where single models might struggle due to their complexity or the diversity of the data. By blending gradients, it’s possible to achieve a more nuanced update of model weights, enhancing the model’s ability to learn from the data and ultimately improving prediction accuracy and generalization.