Reinforcement learning (RL) post-training has emerged as the key paradigm for improving autoregressive video models. Emerging vision-language reward models further strengthen post-training with accurate rewards, but their long chain-of-thought inference substantially raises the associated costs, while the pursuit of long videos prolongs policy training. Despite these rising costs, existing video RL systems still adopt coarse-grained stage-centric execution, which stalls policy training until all reward evaluation finishes, replays every trajectory twice due to a further barrier within policy training, and induces load imbalance across GPUs. To address the challenges, we propose StageBreaker, a fine-grained data-dependency-driven scheduling framework, which schedules the true dataflow of an RL step rather than its stages. Specifically, StageBreaker overlaps policy training with reward evaluation by exploiting reward-separable policy training, and merges the two trajectory replays into one by leveraging loss-independent back-propagation. It further balances GPU load by scheduling computation at the finer granularity. Our evaluation results show that StageBreaker achieves a speedup of up to 1.41$\times$ over stage-centric execution.

翻译：暂无翻译