Author

Hai Ci

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#machine learning Mar 2026

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning

Goal-Driven Data Optimization (GDO) is presented, a framework that computes six sample descriptors for each candidate and constructs optimized 1$\times$ training subsets for different goals that enables faster convergence with fewer training samples under a fixed training protocol.

Ru-Jie Wu, Haozhe Zhao, Hai Ci et al. · arXiv.org · 0 citations