Tmall Group of Alibaba)

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...

Auto-bidding and budget pacing are formulated as sequential decision-making tasks. While flexible, such a framework faces a fundamental granularity mismatch: decisions are made at a fine temporal scale, while their performance feedback is fully and reliably observable at a much coarser resolution...