3 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
GameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection
Chaobo Jia, Ruipeng Wan, Ting Sun +5
LLM-based game generation promises to turn natural-language specifications into executable games, but progress is limited by the lack of reliable automated verification. Unlike con…
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…
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…
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
Borui Wan, Juntao Zhao, Chuan Wu
Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and emb…