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About this paper
Li Peng, Chenghuan Huang, Ziyang Ma, Jing Lyu, Xiang Chen, Jie Zhang
StageBreaker schedules reinforcement-learning post-training for chunk-by-chunk video generation by starting policy-gradient work when its inputs are ready, rather than waiting for stages. It combines that work with reward-derived advantages, merges replay to avoid a second pass, and moves tasks to available GPUs. With DanceGRPO and VR-Thinker at 24 videos, it achieved 1.41× speedup over Colocated and 1.81× over AsyncReward in RL-step latency.
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