Author

Yingqi Peng

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Preprint Sep 2026

AReaL-TIK: Stateful Agentic Optimization of Unified RL Kernels through an Optimization IR

Reinforcement learning (RL) post-training often uses distinct GPU kernels for rollout and policy update. In synchronous PPO and GRPO, numerical disagreement can perturb ratios between current token probabilities and those assigned during rollout. Recomputing rollout log-probabilities with the policy-update backend avoi...

Ran Yan, You-He Jiang, Jia-Yi Nie et al. · 0 citations

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