arXiv AI October 31, 2026 By Michael Y. Hu, Apurva Gandhi, Kyunghyun Cho, Tal Linzen, Pratyusha Sharma

Always Learning, Always Mixing: Efficient and Simple Data Mixing All the Time

The paper introduces OP‑Mix, an on‑policy data mixing algorithm that operates continuously across pretraining, mid‑training, and instruction tuning. By simulating candidate mixtures through low‑rank adapters trained on the current model, OP‑Mix avoids separate proxy models and remains grounded in the model’s learning dynamics. Experiments show OP‑Mix consistently finds near‑optimal mixtures, improving pretraining perplexity by 6.3% and reducing compute for continual learning by 66–95% compared to baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Hugging Face Trending Papers

Oct 5

Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning

The paper demonstrates that off‑policy merging, called grafting, outperforms on‑policy self‑distillation for continual learning. Grafting learns updates on an earlier donor checkpoint, scales the update, and optionally masks sensitive directions, thereby reducing interference with existing capabilities. Across various continual learning scenarios, grafting achieves better new‑task and old‑task performance without costly on‑policy sampling.

reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jul 10

When Does Continual Learning Require Learning

arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?

By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 16

AC-ODM: Actor--Critic Online Data Mixing for Sample-Efficient LLM Pretraining

arXiv:2505. 23878v2 Announce Type: replace-cross Abstract: Optimizing pretraining data composition is pivotal for LLM generalization.

By Jing Ma, Chenhao Dang, Mingjie Liao

llms reinforcement-learning benchmarks

arXiv Computation and Language

Oct 1

DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

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

llms rag benchmarks

arXiv Machine Learning

Jul 20

Rethinking Transfer in Continual Learning: A Replay-Based Realisation

arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.

By Yang Meng, Zhenya Liu, Zhuokai Zhao, Yuxin Chen

llms efficiency

arXiv AI

Oct 28

Advancement or Acceleration? How Data Curation Improves LLM Training

arXiv:2610. 36983v1 Announce Type: cross Abstract: While data curation is widely credited with improving large language model training, a basic question remains unresolved: does curation expand what a model can ultimately learn, or merely reach the same solution faster?

By Jiaxing Wang, Zicheng Zhang, Deping Xiang, Jin Xu, Yong Li, Chao Liu, Pengzhang Liu, Qixia Jiang

llms