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cs.DC2026
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
Chenhao Ye, Huaizheng Zhang, Mingcong Han +11
Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computational resources. However, existing…
cs.DC2025
Role-Based Fault Tolerance System for LLM RL Post-Training
Zhenqian Chen, Baoquan Zhong, Xiang Li +6
RL post-training for LLMs has been widely scaled to enhance reasoning and tool-using capabilities. However, RL post-training interleaves training and inference workloads, exposing…
cs.DC2025
Fast LLM Post-training via Decoupled and Fastest-of-N Speculation
Rongxin Cheng, Kai Zhou, Xingda Wei +8
Rollout dominates the training time in large language model (LLM) post-training, where the trained model is used to generate tokens given a batch of prompts. This work, SpecActor,…