Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
<|reference_start|>Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization: In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets, with a specific focus on selective retention of samples that incur moderately high losses. These samples are deemed informative and beneficial for model refinement, contrasting with the highest-loss samples, which would be discarded due to their correlation with data noise and complexity. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization (IR-DRO). IR-DRO is designed to dynamically prioritize the training focus on informative samples through an instance reweighting mechanism, streamlined by a closed-form solution for straightforward integration into established training protocols. Through rigorous experimentation with various models and datasets, our findings indicate that our sample-targeted methods significantly improve LLM performance across multiple benchmarks, in both continual pre-training and instruction tuning scenarios. Our codes are available at https://github.com/VITA-Group/HardFocusTraining.<|reference_end|>
@article{chen2024take,
    title={Take the Bull by the Horns: Hard Sample-Reweighted Continual Training
  Improves LLM Generalization},
    author={Xuxi Chen, Zhendong Wang, Daouda Sow, Junjie Yang, Tianlong Chen,
  Yingbin Liang, Mingyuan Zhou, Zhangyang Wang},
    journal={arXiv preprint arXiv:2402.14270},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14270},
    primaryClass={cs.LG}
}