"Fine-tuning can restore performance on newly introduced data distributions without establishing unrestricted reasoning generalization."
Zhao and colleagues found that supervised fine-tuning on portions of previously unseen data could rapidly improve performance on those specific distributions. They interpret this as enlarging the model's familiar distribution rather than demonstrating a general ability to infer unseen logical transformations, strengthening the section's treatment of bounded model capabilities.
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