1 citations · 1 across the 10 of their papers we have counts for
11 papers
SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
Dongfang Li, Xiaodong Luo, Ruoyu Sun +62
Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pre…
ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload
Ziyue Liu, Zhengyang Wang, Ruijie Zhang +7
Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient training systems. Yet existing framewor…
MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization
Yupeng Su, Ruijie Zhang, Ziyue Liu +2
The Muon optimizer has emerged as a compelling alternative to Adam for training large language models, achieving remarkable computational savings through gradient orthogonalization…
RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning
Jiayi Tian, Yupeng Su, Ryan Solgi +2
Large reasoning models (LRMs) enhance problem-solving capabilities by generating explicit multi-step chains of thought (CoT) reasoning; however, they incur substantial inference la…
Muon: Boosting Muon via Adaptive Second-Moment Preconditioning
Ziyue Liu, Ruijie Zhang, Zhengyang Wang +4
Muon has emerged as a promising optimizer for large-scale foundation model pre-training by exploiting the matrix structure of neural network updates through iterative orthogonaliza…
SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs
Jin Lee, Zhonghao Chen, Xuhang He +6
In large-scale LLM pre-training systems with 100k+ GPUs, failures become the norm rather than the exception, and restart costs can dominate wall-clock training time. However, exist…
TEON: Tensorized Orthonormalization Beyond Layer-Wise Muon for Large Language Model Pre-Training
Ruijie Zhang, Yequan Zhao, Ziyue Liu +5
The Muon optimizer has demonstrated strong empirical performance in pre-training large language models by performing matrix-level gradient (or momentum) orthogonalization in each l…
BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models
Zhengyang Wang, Ziyue Liu, Ruijie Zhang +5
The scale of transformer model pre-training is constrained by the increasing computation and communication cost. Low-rank bottleneck architectures offer a promising solution to sig…
LaX: Boosting Low-Rank Training of Foundation Models via Latent Crossing
Ruijie Zhang, Ziyue Liu, Zhengyang Wang +1
Training foundation models such as ViTs and LLMs requires tremendous computing cost. Low-rank matrix or tensor factorization offers a parameter-efficient alternative, but often dow…
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
Ziyue Liu, Ruijie Zhang, Zhengyang Wang +7
The full-size MLPs and the projection layers in attention introduce tremendous model sizes of large language models (LLMs), consuming extensive computational resources in pre-train…
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
Zi Yang, Ziyue Liu, Samridhi Choudhary +4
Training large AI models such as LLMs and DLRMs costs massive GPUs and computing time. The high training cost has become only affordable to big tech companies, meanwhile also causi…