GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their immense number of parameters and complex transformer-based architectures result in significant resource demands and computational complexity during training, making it challenging to optimize them efficiently on large datasets. To reduce training costs while preserving performance, researchers have investigated coreset selection techniques, which aim to identify small, representative subsets of the entire training dataset to accelerate LLM training. However, existing coreset selection methods fail to adapt to the dynamic nature of LLM training and often s...
| Authors | Tianhao Tang, Haoyang Li, Lei Chen |
|---|---|
| Primary category | cs.DB |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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