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This paper introduces Perturbation Gradient (PG) losses, a new family of differentiable surrogate losses for decision-focused learning within the predict-then-optimize framework. These losses are shown to be Lipschitz continuous and provide asymptotic guarantees for vanishing excess regret, even under model misspecification, outperforming existing benchmarks in such challenging scenarios.