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Reshaping Rollout Workloads for Asynchronous RL Post-Training on Heterogeneous Accelerators
Reinforcement learning (RL) post-training increasingly relies on long-horizon, multi-turn rollouts. As post-training jobs outgrow a single cluster, rollout pools assembled across clusters introduce hardware heterogeneity. Rollout scheduling must serve two stakeholders: the hardware needs high aggreg
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