C. Qian, X. Fu, N.D. Sidiropoulos, L. Huang, and J. Xie explore phase retrieval in the presence of outliers in their paper published in the IEEE Transactions on Signal Processing. Typically, phase retrieval algorithms perform well under Gaussian noise; however, their performance severely degrades with significant data corruption. This study investigates heavy-tailed phase retrieval techniques, proposing p-norm estimators (0 < p>imprecise alternating optimization are introduced to tackle the resulting optimization problem. Notably, the core minimization step can be interpreted as iterative reweighted least squares and gradient descent. The authors discuss the convergence properties of the algorithms and derive the Cramer-Rao Bound (CRB). Simulations demonstrate that the proposed algorithms are effective and close to optimal.