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Related Questions
- Can high learning rates lead to overfitting in a fine-tuned model, and why?
- How can we balance high learning rates with the risk of the model memorizing the training data?
- Is there an optimal learning rate range for fine-tuned models, and how to determine it?
- Will a high learning rate only improve the performance of overfitting models, but not underfitting models?
- What does it mean to say 'generalization gap' increases when using high learning rate during fine-tuning in the context of large-language models?
- We observed that with increasing complexity of pre-trained model the large generalization gap widens and learning rates get further amplified, can we elaborate this phenomenon and why a pre-trained model has wider gap in contrast to randomly initialized parameter.
- In this fine-tuning setting could learning rate schedules with initial slow increase have some performance benefit compared to learning that linearly ramped for several epochs and after we increase it and the overall behavior of these schedule vary for large versus small-size language models.
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