AI RESEARCH
Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning
arXiv CS.AI
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ArXi:2605.12906v1 Announce Type: cross Data selection during supervised fine-tuning (SFT) can critically change the behavior of large language models (LLMs). Although existing work has studied the effect of selecting data based on heuristics such as perplexity, difficulty, or length, the reported findings are often inconsistent or context-dependent.