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This work was supported in part by the National Natural Science Foundation of China under Grant 62371310, in part by the Shenzhen Science and Technology Program (JCYJ20241202124415021), in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515011236. (Corresponding author: Xu Wang.)
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Achieve significant cost savings in LLM reinforcement learning by overlapping rollout generation, dissemination, and training with a framework that tolerates bounded policy staleness.
