Author

Ruida Xu

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Preprint Aug 2026

AReaL-DTE: Sparse Policy-Weight Transfer for Online Agentic Reinforcement Learning

AReaL-DTE is presented, a snapshot-free Delta Transfer Engine that translates inference-visible weight sparsity into end-to-end system efficiency and achieves speedups of up to 19.9x over ByteCheckpoint and 3.2x over PULSE across clusters, and up to 7.6x and 7.4x within a cluster.

Yingqi Peng, Jia-Wei Zhang, Wenhao Zhou et al. · 1 citation