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cs.DC2026
Accelerating MoE Training through Expert Placement and Replica Coordination
Zhiqiang Tan, Maoxin Wang, Wenxiang Lin +4
Distributed training of sparsely activated mixture-of-experts (MoE) models is often bottlenecked by workload imbalance and all-to-all (A2A) communication induced by dynamic token r…
cs.DC2026
Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training
Zhiqiang Tan, Maoxin Wang, Sijie Wang +4
It is well established that the reasoning capabilities of large language models (LLMs) can be improved by applying reinforcement learning (RL) in a post-training stage. In a standa…