Optimal data difficulty for LLM supervised fine-tuning shifts toward harder examples as data budget increases due to the generalization-extrapolation tradeoff.
Mathematical Association of America , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GIFT guides adapter fine-tuning on base models with confidence signals from instruction-tuned models before merging, yielding task-specialized models that outperform direct fine-tuning on math and knowledge benchmarks.
citing papers explorer
-
Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning
Optimal data difficulty for LLM supervised fine-tuning shifts toward harder examples as data budget increases due to the generalization-extrapolation tradeoff.
-
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
GIFT guides adapter fine-tuning on base models with confidence signals from instruction-tuned models before merging, yielding task-specialized models that outperform direct fine-tuning on math and knowledge benchmarks.