arXiv:2605.30537v1 Announce Type: new Abstract: Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with recent methods prioritizing samples by current utility, diversity, quality, or influence. This paper studies a different question: when fine-tuning occurs over multiple stages, can selection strategies that look optimal now make the model less adaptable later? We introduce a long-horizon view of LLM data selection in which a selector is evaluated not only by immediate task performance, but also by future adaptation speed, forgetting, capability
This research emerges as LLM fine-tuning becomes a standard practice and the industry grapples with optimizing cost-efficiency and long-term model adaptability.
A strategic reader should care because suboptimal data selection strategies can lead to models that degrade over time or become less adaptable to future tasks, impacting long-term R&D efficiency and model performance.
The focus in LLM fine-tuning data selection shifts from immediate performance gains to considering long-term adaptability and potential 'forgetting,' requiring more sophisticated evaluation metrics.
- · AI research labs focused on model longevity
- · Companies with diverse and high-quality data pipelines
- · Developers of advanced data selection algorithms
- · LLM operators focused on continuous deployment
- · LLM developers solely optimizing for short-term performance
- · Companies with limited or low-quality data sets
- · Organizations relying on static fine-tuning approaches
Research into dynamic and long-horizon data selection techniques for LLMs will intensify.
The cost and complexity of ensuring LLM adaptability over time will increase, potentially consolidating development among larger players.
Future AI systems may incorporate 'meta-learning' capabilities for data selection, enabling them to self-optimize their training data over their lifecycle.
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Read at arXiv cs.LG