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cs.DC2026
Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Start
Xueshen Liu, Yongji Wu, Yuncheng Yao +3
Modern LLM service providers increasingly rely on autoscaling and parallelism reconfiguration to respond to rapidly changing workloads, but cold-start latency remains a major bottl…
cs.DC2026
RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs
Yongji Wu, Xueshen Liu, Haizhong Zheng +5
Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows involve interleaving rollout and traini…