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This article interprets the paper "Exact Tail Laws for Nonlinear SGD with Infinite-Variance Gradients," analyzing exact tail probabilities of SGD under infinite-variance gradients, core theoretical results, and experimental validation, and exploring the impact of heavy-tailed noise on optimization algorithms and practical implications.