arXiv AI

Piper: A Programmable Distributed Training System

arXiv:2606. 11169v1 Announce Type: cross Abstract: Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO.

arXiv AI

Jul 23

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

arXiv:2607. 20145v1 Announce Type: cross Abstract: Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution.

By Dongfang Li, Xiaodong Luo, Ruoyu Sun, Xuhui Chen, Linyuan Qiu, Jian Meng, Zhengxuan Lu, Yiting Wang, Yucheng Xie, Tao Guo, Tianxiang Fang, Jing Li, Sihang Chen, Shihao Hong, Chang Liu, Weihua Dai, Zirong Zeng, Ziwei Zhu, Zhuohan Wang, Zhengjun Yue, Igor Vasilyev, Min Liu, Weijian Sun, Xin Chen, Yingmeng Gao, Jinhua Zhou, Taolue Chen, Chenwei Wu, Dong Zhang, Wenlong Jin, Jinmin Xiang, Barkova Maria, Ushakov Anton, Xianfei Jin, Tian Ding, Zhihang Lin, Qian Chen, Linxin Yang, Mingzhe Yang, Bingwei Zhang, Hongzhang Yang, Fangxue Zhang, Shijun Qin, Jie Yu, Cuihua Hu, Tolstykh Vasiliy, Nosov Ivan, Abdullin Amir, Zhichen Zhou, Xin Zhang, Zhixiong Ning, Xutong Zhao, Junjie Huang, Jiajun Liu, Weiyan Kong, Zheng Zhang, Wenhan Luo, Lin Hu, Yangbo Guo, Li Zeng, Shihao Zeng, Baotian Hu, Min Zhang, Haizhou Li, Zhiquan Luo

llms agents fine-tuning

arXiv Machine Learning

Jun 9

Efficient Scaling of LLM Training with Flexible Context Parallelism

arXiv:2602. 21788v2 Announce Type: replace-cross Abstract: Scaling long-context capabilities is crucial for Large Language Models (LLMs).

By Yifan Niu, Han Xiao, Dongyi Liu, Wei Zhou, Jia Li

llms efficiency

arXiv AI

Aug 11

Training Variable Long Sequences with Data-Centric Parallel

arXiv:2608. 07524v1 Announce Type: new Abstract: Training deep learning models on variable long sequences poses significant computational challenges.

By Geng Zhang, Xuanlei Zhao, Kai Wang, Yang You

arXiv Machine Learning

Jul 21

DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training

arXiv:2604. 26256v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed generation: long-tailed trajectories indispensable for model performance block the entire training pipeline.

By Tianhao Hu, Xiangcheng Liu, Yuchun Miao, Youshao Xiao, Hongyu Zang, Yang Zheng, Xuan Huang, Jinrui Ding, Yufei Zhang, Yu Yang, Yi-Kai Zhang, Yueqing Sun, Chengcheng Han, Xiandi Ma, Wei Wang, Qi Gu, Yerui Sun, Yuchen Xie, Xunliang Cai

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Jul 7

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling

arXiv:2509. 23722v2 Announce Type: replace-cross Abstract: Pipeline parallelism is widely used to train large language models (LLMs).

By Jihu Guo, Tenghui Ma, Wei Gao, Peng Sun, Xun Chen, Jiaxing Li, Zhisheng Ye, Yuyang Jin, Dahua Lin

llms efficiency benchmarks

arXiv Machine Learning

Jun 3

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

arXiv:2606. 02963v1 Announce Type: new Abstract: Production inference increasingly targets a heterogeneous mix of accelerators.

By Taras Sereda, Burak Bartan, Ankita Nayak, Tom St. John, Natalie Serrino, Zain Asgar

llms agents efficiency benchmarks

arXiv Machine Learning

Aug 5

When RL Meets Adaptive Speculative Training: A Unified Training-Serving System

arXiv:2602. 06932v5 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating speculator training as a standalone offline modeling problem.

By Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Ce Zhang, Tri Dao, Percy Liang, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu

llms reinforcement-learning

arXiv Machine Learning

Jun 26

DMuon: Efficient Distributed Muon Training with Near-Adam Overhead

arXiv:2606. 27153v1 Announce Type: cross Abstract: Matrix-orthogonalization-based optimizers, exemplified by Muon, have demonstrated strong convergence behavior across a wide range of modern deep learning workloads.

By Vincent Chen, Starrick Liu, Regis Cheng, Dance Yang, Shalfun Li, Ryan Yu, Lucy Liang, Hang Su, Roy Gan, Hao Wang, Qian Wang

llms robotics

arXiv AI

Jun 9

FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training

arXiv:2606. 08476v1 Announce Type: cross Abstract: Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead.

By Zheng Wang, Eric Liu, Linan Jiang, Zhongkai Yu, Zaifeng Pan, Yue Guan, Yuke Wang, Yufei Ding

llms benchmarks

arXiv Machine Learning

Aug 4

Nova: An End-to-End MLIR Compiler for Deep Learning

arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.

By Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra

efficiency

arXiv Machine Learning

Jun 2

Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap

arXiv:2512. 10236v2 Announce Type: replace-cross Abstract: Modern ML workloads demand distributing training and inference across multiple GPUs.

By Shagnik Pal, Shaizeen Aga, Suchita Pati, Mahzabeen Islam, Lizy K. John

arXiv AI

Jul 3

Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models

arXiv:2607. 01844v1 Announce Type: cross Abstract: This paper showcases a memory-efficient training stack for Mixture-of-Experts (MoE) models.

By Xuan-Phi Nguyen, Shrey Pandit, Yiran Zhao, Semih Yavuz, Silvio Savarese, Shafiq Joty

fine-tuning efficiency benchmarks

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