45 citations · 55 across the 12 of their papers we have counts for
8 papers · 1 filter
Towards Full Pipeline FP8 Reinforcement Learning for LLMs
Fanchao Chen, Ziheng Jiang, Ziyun Wei +5
Reinforcement learning (RL) has become a key technique for improving the reasoning and agentic abilities of large language models (LLMs). Although FP8 quantization can accelerate R…
DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism
Zhichen Zeng, Chi-Chih Chang, Jiayi Wang +10
Mixture-of-experts (MoE) architectures enable trillion-parameter LLMs with sparsely activated experts. Expert parallelism (EP) is a widely adopted MoE training strategy, but it suf…
Robust LLM Training Infrastructure at ByteDance
Borui Wan, Gaohong Liu, Zuquan Song +32
The training scale of large language models (LLMs) has reached tens of thousands of GPUs and is still continuously expanding, enabling faster learning of larger models. Accompanyin…
Laminar: A Scalable Asynchronous RL Post-Training Framework
Guangming Sheng, Yuxuan Tong, Borui Wan +10
Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performanc…
HybridFlow: A Flexible and Efficient RLHF Framework
Guangming Sheng, Chi Zhang, Zilingfeng Ye +6
Reinforcement Learning from Human Feedback (RLHF) is widely used in Large Language Model (LLM) alignment. Traditional RL can be modeled as a dataflow, where each node represents co…
QSync: Quantization-Minimized Synchronous Distributed Training Across Hybrid Devices
Juntao Zhao, Borui Wan, Yanghua Peng +3
A number of production deep learning clusters have attempted to explore inference hardware for DNN training, at the off-peak serving hours with many inference GPUs idling. Conducti…
LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
Juntao Zhao, Borui Wan, Yanghua Peng +2
Recent breakthroughs in Large-scale language models (LLMs) have demonstrated impressive performance on various tasks. The immense sizes of LLMs have led to very high resource deman…
CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs
Hanpeng Hu, Junwei Su, Juntao Zhao +4
Deep Neural Networks (DNNs) have shown excellent performance in a wide range of machine learning applications. Knowing the latency of running a DNN model or tensor program on a spe…