Training of Structural SVMs involves solving a large Quadratic Program (QP). One popular method for solving this QP is a cutting-plane approach, where the most violated constraint is iteratively added to a working-set of constraints. Unfortunately, training models with a large number of parameters remains a time consuming process. This paper shows that significant computational savings can be achieved by adding multiple diverse and highly violated constraints at every iteration of the cutting-plane algorithm. We show that generation of such diverse cutting planes involves extracting diverse M-Best solutions from the loss-augmented score of the training instances. To find these diverse M-Best solutions, we employ a recently proposed algorithm . Our experiments on image segmentation and protein side-chain prediction show that the proposed approach can lead to significant computational savings, e.g., ∼28% reduction in training time.