Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning

Instruction-tuning datasets for large language models (LLMs) are often large, redundant, and imbalanced, limiting efficient adaptation. Naive large-batch fine-tuning repeatedly includes overrepresented sample groups while weakly covering underrepresented but informative ones, especially under data p