Wei Wang

Rollout–training disaggregation is emerging as the standard architecture for Reinforcement Learning (RL) post-training, where memory-bound rollout and compute-bound training are physically disaggregated onto purpose-built clusters to maximize hardware efficiency. However, the strict synchronizati...

Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation. Existing systems either colocate all stages on a single G...

The rapid scaling of generative AI (GenAI), alongside the continued reliance on classical deep neural networks (DNNs), has pushed production AI infrastructure toward massive, heterogeneous GPU fleets. We present a comprehensive characterization of Alibaba Serverless Infrastructure (ASI), a hypers...

Federated low-rank adaptation (LoRA) enables multiple clients to collaboratively fine-tune large language models (LLMs) without disclosing their raw data. However, existing works often experience performance degradation due to biased model aggregation and are hindered by significant communication...