AINeutralarXiv – CS AI · 7h ago6/10
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Unifying and Optimizing Data Values for Selection via Sequential Decision-Making
Researchers propose a new framework that reinterprets data selection as a sequential decision-making problem rooted in dynamic programming, unifying existing methods like Data Shapley while revealing their limitations as myopic approximations. The work introduces a scalable bipartite graph-based approach that preserves submodular structure and demonstrates improvements on machine learning and LLM fine-tuning tasks.