Unverified50% confidenceFactExact time
监督学习本质上是函数逼近问题，通过最小化损失函数实现，常见损失函数包括回归任务的均方误差（MSE）和分类任务的交叉熵（Cross Entropy）
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8/23/2026
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VerifiedCross-Entropy Loss is commonly used for classification tasks, and Mean Squared Error (MSE) is commonly used for regression tasks.79% similarUnverifiedBehavioral cloning simplifies the problem to supervised learning, but suffers from distribution shift, where differences between the state distributions at training time and execution time lead to error accumulation.70% similarUnverifiedThe core loss functions of contrastive learning (such as InfoNCE and NT-Xent) essentially pull the representations of similar samples closer together while pushing the representations of dissimilar samples further apart.70% similarUnverifiedCross-entropy loss often converges faster than MSE in classification tasks because, when paired with softmax output, gradients do not vanish in the saturation region.68% similar
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