Unverified50% confidenceCautionExact time
The model needs to be trained for a problem distribution, and if the instance distribution changes drastically, retraining may be required.
1
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50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
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Related Claims
UnverifiedEvaluating within the training distribution easily overestimates model capability, while out-of-distribution benchmarks require the model to generalize to unseen scene and interaction combinations, better exposing the gap between 'looking right' and 'being physically right'.76% similarUnverifiedWhen the distribution of online data deviates from the training data, model performance degrades (data drift and model decay), requiring retraining to be triggered.76% similarVerifiedAlignment training optimizes the model's statistical behavior in known scenarios rather than establishing formally verifiable security boundaries, and may fail when faced with rare instruction combinations or role-playing frameworks outside the training distribution.75% similarUnverifiedWhen business tools or the data environment undergo significant changes, the fine-tuned model may need to be retrained, making task stability a core dimension for assessing the value of fine-tuning.74% similarUnverifiedThere is tension between supporting the model's dialogue generalization ability and its domain fit: previously mixing in general data was to remedy the side effects of narrow-domain training, and tightening replay will bring part of that problem back.73% similar
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