4 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
AuroraRL: Fast, Fault-Tolerant, and Cost-Efficient Reinforcement Learning over Decentralized Network
Chaoyi Ruan, Geng Luo, Xinyi Wan +12
LLM reinforcement learning (RL) requires frequent synchronization of large model parameters between the trainer and distributed rollout actors. High-throughput RL post-training the…
Revisiting Parameter Server in LLM Post-Training
Xinyi Wan, Penghui Qi, Guangxing Huang +3
Modern data parallel (DP) training favors collective communication over parameter servers (PS) for its simplicity and efficiency under balanced workloads. However, the balanced wor…
Cortex: Achieving Low-Latency, Cost-Efficient Remote Data Access For LLM via Semantic-Aware Knowledge Caching
Chaoyi Ruan, Chao Bi, Kaiwen Zheng +3
Large Language Model (LLM) agents tackle data-intensive tasks such as deep research and code generation. However, their effectiveness depends on frequent interactions with knowledg…
Balancing Pipeline Parallelism with Vocabulary Parallelism
Man Tsung Yeung, Penghui Qi, Min Lin +1
Pipeline parallelism is widely used to scale the training of transformer-based large language models, various works have been done to improve its throughput and memory footprint. I…
Zero Bubble Pipeline Parallelism
Penghui Qi, Xinyi Wan, Guangxing Huang +1
Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable. In this work,…