Unverified50% confidenceFactExact time
Current alignment mainly adds a preference loss or penalty term on top of the pretraining loss, and most of the model's capacity is still shaped by internet data.
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50%
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Long-term
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10/18/2026
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UnverifiedWhen the distribution of online data deviates from the training data, model performance degrades (data drift and model decay), requiring retraining to be triggered.77% similarUnverifiedThe reason for using a correlation coefficient rather than simply comparing final loss values is that it can capture the dynamic consistency across the entire training run, which proves the numerical validity of the training process better than a single endpoint metric.73% similarVerifiedDuring the post-training phase, an excessively low loss may correspond to overfitting, where the model memorizes the training data and loses generalization ability.72% similarUnverifiedThe upper bound of generation quality driven by trend data depends on the timeliness of training data and the breadth of platforms covered; data lag may cause the system to chase content formats that have already fallen out of favor.71% similar
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