arXiv:2605.12906v2 Announce Type: replace Abstract: 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. In this work, we systematically study the role of data difficulty in fine-tuning from both empirical and theoretical perspectives, and find that there is no universally optimal difficulty level; rather, its effectiveness depends on the
The rapid advancement and widespread deployment of LLMs necessitate a deeper understanding of fine-tuning techniques to optimize performance and resource utilization.
Optimizing data selection for LLM fine-tuning can significantly impact the models' capabilities, efficiency, and generalization, which is crucial for building robust and adaptable AI systems.
The understanding of LLM fine-tuning strategies shifts from heuristic-based approaches to a more systematic and context-dependent understanding of data difficulty, challenging previous assumptions.
- · AI researchers
- · LLM developers
- · Companies with proprietary data
- · Developers relying on generic fine-tuning methods
- · Organizations without sophisticated data curation pipelines
Refined fine-tuning methodologies will lead to more efficient and powerful custom LLMs.
Improved model performance with less data could reduce compute requirements for specialized AI tasks.
The development of highly specialized and efficient LLMs could accelerate the deployment of intelligent agents in various industries.
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Read at arXiv cs.LG