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This lecture delves into stochastic softmax tricks, focusing on the reparametrization trick and the stochastic argmax trick. It explores the challenges of expectation estimation and provides examples of optimizing paths in different scenarios. The lecture also discusses the high variance in gradient estimation and the use of relaxed estimators to reduce it.