Junhao Hu

Reinforcement learning (RL) has demonstrated immense potential in advancing artificial general intelligence, agentic intelligence, and embodied intelligence. However, the inherent heterogeneity and dynamicity of RL workflows often lead to low hardware utilization and slow training on existing sys...

Modern reinforcement learning (RL) workloads, powering large language models, long‑horizon reasoning, and agentic systems, exhibit extreme dynamicity due to heavy‑tailed rollouts, irregular multi‑turn tool interactions, and time‑varying bottlenecks. Static resource allocations in today’s distribu...

Large Language Models (LLMs) are increasingly deployed in agent-based applications with complex prompt structures comprising both invariant and dynamic segments. Existing KV cache reuse strategies—PositionDependent Caching (PDC) and Position-Independent Caching (PIC)—inadequately address these s...