The article proposes treating large language model (LLM) data mixing as a classical mixture experiment, where data domains are components, token shares are proportions, and proxy-training runs serve as design points. Using sparse second‑order Scheffé response‑surface models, the authors construct model‑robust Σ‑optimal designs that efficiently identify optimal data mixtures and reveal strong interaction effects, especially between weak domains and web‑derived text. Empirical results on RegMix show that these designs recover mixture rankings while reducing proxy runs by about 25%, demonstrating that data mixing can be optimized through experimental design rather than solely prediction.
By Yicheng Mao, Hongru Du
arXiv:2606. 14971v1 Announce Type: cross Abstract: While large and diverse datasets have driven recent advances in large models, identifying the optimal data mixture for pre-training and post-training remains a significant open problem.
By Haoru Tan, Sitong Wu, Yanfeng Chen, Jun Xia, Ruobing Xie, Bin Xia, Xingwu Sun, Xiaojuan Qi
arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.
By Zinan Tang, Yukun Zhang, Shaomian Zheng, Zhuoshi Pan, Qizhi Pei, Dingnan Jin, Jun Zhou, Yujun Wang, Biqing Huang
arXiv:2607. 22769v1 Announce Type: cross Abstract: The training efficacy of large language models (LLMs) is fundamentally constrained by the quality and composition of training data.
By He Zhang
arXiv:2603.26164v2 Announce Type: replace-cross
Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters...
By Hao Liang, Zhengyang Zhao, Mingrui Chen, Meiyi Qiang, Lu Ma, Rongyi Yu, Hengyi Feng, Shixuan Sun, Zimo Meng, Xiaochen Ma, Xuanlin Yang, Qifeng Cai, Ruichuan An, Bohan Zeng, Zhen Hao Wong, Chengyu Shen, Runming He, Zhaoyang Han, Yaowei Zheng, Fangcheng Fu, Conghui He, Bin Cui, Zhiyu Li, Weinan E, Wentao Zhang
arXiv:2606. 16246v1 Announce Type: cross Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, compute-abundant regime that demands productive multi-epoch training on fixed corpora.
By Michael K. Chen, Xikun Zhang, Zhen Wang
arXiv:2606. 07597v1 Announce Type: cross Abstract: Pre-training data mixtures are commonly tuned by running small-scale experiments and extrapolating to the target training budget.
By Kevin Zhou, Lisa Alazraki, Kris Cao, Marek Rei
The paper investigates training-time data augmentation as a regularizer for autoregressive language model pretraining in data‑constrained, compute‑abundant settings. It introduces three orthogonal augmentation categories—token‑level noise, sequence permutations, and target offset prediction—and shows through systematic ablations that each category delays overfitting and reduces validation loss, with random token replacement performing best individually. Combining augmentation categories further lowers the minimum validation loss, demonstrating that such augmentations mitigate data inefficiency in autoregressive pretraining.
By Michael K. Chen, Xikun Zhang, Fan Bai, Zhengding Hu, Zhen Wang
arXiv:2609.37169v1 Announce Type: cross
Abstract: Mid-training equips pretrained large language models with specialized and reasoning capabilities, but the returns of this stage are bounded since add...
By Zhehao Huang, Changxin Tian, Qingyuan Yang, Kunlong Chen, Ziqi Liu, Zhiqiang Zhang, Xiaolin Huang, Jun Zhou
arXiv:2608. 13277v1 Announce Type: cross Abstract: We ask whether language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model.
By Mohammed Sabry, Sean Augenstein, Keith Rush, Lucio Dery
The paper introduces OTROPE, a likelihood‑free method for off‑policy evaluation of large language models (LLMs) that uses optimal transport to align labeled samples from a behavior model with unlabeled samples from a target model in a semantic space. OTROPE corrects human‑labeled residuals with proxy predictors, achieving a doubly robust evaluation without requiring behavior‑policy modeling or density‑ratio estimation. The authors provide theoretical guarantees for consistency and convergence, and demonstrate through synthetic and real LLM tasks that OTROPE outperforms existing baselines and can elevate weaker evaluators to match or exceed stronger ones.
By Liner Xiang, Wenbo Zhang, Hengrui Cai
arXiv:2602. 19938v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets.
By Zijie Liu, Jie Peng, Jinhao Duan, Zirui Liu, Kaixiong Zhou, Mingfu Liang, Luke Simon, Xi Liu, Zhaozhuo Xu, Tianlong Chen