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Si-Ran Yang

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W EAVE : Efficient Co-Scheduling for Disaggregated RL Post-Training

W EAVE is a cluster scheduling framework built on the insight that the structural idleness of one job can be effectively utilized by the active phase of another, and introduces the co-execution group abstraction, which partitions the cluster into isolated locality domains.

Tianyuan Wu, Lunxi Cao, Yi-Chen Wei et al. · 3 citations
#natural language process... Preprint Sep 2026

PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition

PCoMoE is presented, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grained path composition and achieves up to a 1.31x end-to-end inference speedup while enhancing model accuracy by 10%.

Zi-Yan Gan, Fangxin Liu, Chenyang Guan 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.

Hanfeng Lu, Tianyu Feng, Suyi Li et al. · 0 citations

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