5 papers
MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
Chunyu Xue, Yangrui Chen, Jianyu Jiang +14
As the foundational component of versatile AI applications, training an multimodal large language model (MLLM) relies on multimodal datasets with dynamic modality mixture proportio…
FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning
Yuyang Ding, Chi Zhang, Juntao Li +2
Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models (LLMs). In this context,…
MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production
Chao Jin, Ziheng Jiang, Zhihao Bai +16
We present MegaScale-MoE, a production system tailored for the efficient training of large-scale mixture-of-experts (MoE) models. MoE emerges as a promising architecture to scale l…
MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism
Ruidong Zhu, Ziheng Jiang, Chao Jin +17
Mixture-of-Experts (MoE) showcases tremendous potential to scale large language models (LLMs) with enhanced performance and reduced computational complexity. However, its sparsely…
FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion
Li-Wen Chang, Wenlei Bao, Qi Hou +10
Large deep learning models have demonstrated strong ability to solve many tasks across a wide range of applications. Those large models typically require training and inference to…