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About this paper
Yubing Bao, Zhihui Lu, Qiang Duan, Yuedong Xu, Sen Liu, Pan Zhou
AugTree targets context parallelism for reinforcement-learning post-training by treating a prompt and its sampled responses as a shared-prefix tree. It computes the prompt’s key–value states once, then selects response-branch placement and communication based on the workload. Across 20 workload–token-budget settings, it averages 1.18× faster update-stage step time than DCP; DCP wins four settings, and this is not a full-pipeline speedup.
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