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In online episodic tabular MDPs, the loss function is adversarial, the environment can choose an arbitrary loss at each round, and the feedback is bandit-style, i.e., only the loss of the chosen action can be observed.
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10/12/2026
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UnverifiedCommonly used loss functions for contrastive learning include InfoNCE Loss and Triplet Loss.66% similarUnverifiedSetting ignore_index in the loss function to point to the padding index can mask the contribution of padding positions to the loss; PyTorch's CrossEntropyLoss supports this parameter.63% similarUnverifiedThe reconstruction loss only compares predictions with ground truth inside the hole; the mask M zeros out pixels the network has already seen, preventing the model from scoring by copying the context.62% similarUnverifiedState compensation strategies can replace failed external API calls with cached data or degraded results, allowing the process to continue moving forward.62% similarUnverifiedNegative Prompts can explicitly tell the model which elements are not desired, so as to avoid results skewing toward a realistic style.62% similar
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