The training loss drops steadily during fine-tuning.But the translated sentences are grammatically wrong.BLEU and other quality metrics do not improve.It feels like the model is optimizing the wrong thing.
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The agent performs well in simulation.When deployed in the real world, it makes strange decisions.The physics is slightly different.Small changes lead to big failures.
My CNN reaches over 95% accuracy on the training set.But on the test set it drops below 40%.The data comes from the same source.I feel the model is memorizing instead of learning.
My image classifier performs very well on bright daylight photos.When images are darker or taken indoors, accuracy drops sharply.The objects are still the same.Only the lighting seems different.
My speech-to-text model produces accurate transcripts when tested in a quiet office.However, when I try to use it in public places, accuracy drops sharply.Background noise causes words to be skipped or misheard.The model feels fragile outside controlled ...