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Ju Huang

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#artificial intelligence Preprint Sep 2026

PEARL: Adaptive Prefill-Decode Execution with Elasticity for Agentic Reinforcement Learning

PEARL is an asynchronous agentic RL system that coordinates external resource elasticity, temporary reuse of idle training GPUs, and adaptive PD execution, and maintains a unified GPU--worker--role state and uses runtime profiles to predict rollout batch completion time.

Ji-Aan Zhu, Wei Gao, You-Hui Bai et al. · 0 citations
Preprint Aug 2026

Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training

Rollplex is presented, a runtime that decomposes the reference and training phase and moves the prefix computation into the rollout decode window and achieves speedup over serial colocation and disaggregation under the same GPU budget, while preserving the synchronous RL update.

Han-Feng Lu, Tian-Yu Feng, Su-Yi Li et al. · 1 citation

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