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cs.LG2026
CausalMix: Data Mixture as Causal Inference for Language Model Training
Zinan Tang, Yukun Zhang, Shaomian Zheng +7
In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via proxy models, but they rely o…
cs.LG2026
On Representation Redundancy in Large-Scale Instruction Tuning Data Selection
Youwei Shu, Shaomian Zheng, Dingnan Jin +5
Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high-quality datasets can outperform those trained on…
cs.LG2026
UniGeM: Unifying Data Mixing and Selection via Geometric Exploration and Mining
Changhao Wang, Yunfei Yu, Xinhao Yao +5
The scaling of Large Language Models (LLMs) is increasingly limited by data quality. Most methods handle data mixing and sample selection separately, which can break the structure…