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

[2608.14498] Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14498 (cs) [Submitted on 14 Aug 2026] Title: Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training Authors: Hanfeng Lu , Tianyu Feng , Suyi Li , Yuheng Zhao , Wei Gao , Shaopan Xiong , Ju Huang , Siran Yang , Jiamang Wang , Lin Qu , Wei Wang View a PDF of the paper titled Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training, by Hanfeng Lu and 10 other authors View PDF HTML (experimental) Abstract: Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) post-training enhances these capabilities using task feedback, but current on-policy RL runtimes execute rollout, reference scoring, and actor training in strict serial phases. While effective for text-only RL, this phase-granular execution is wasteful for VLMs, where processing dense video inputs and prompt prefixes occupies a large fraction of each phase. Because prefix processing is independent of the generated response, it can be run alongside rollout decoding, which leaves GPU compute capacity underutilized, without breaking synchronous on-policy semantics. We present Rollplex, a runtime that decomposes the reference and training phase and moves the prefix computation into the rollout decode window. Realizing this schedule requires more than concurrent kernel launches: naive colocation of Qwen2.5-VL-32\,B requires roughly 165\,GiB per GPU, while rollout and training prefer different tensor-parallel (TP) degrees and weight layouts. Rollplex addresses these constraints with two mechanisms. Phase-aware memory management controls HBM residency according to producer--consumer lifetimes. Parallelism-aware weight sharing uses the same physical storage for layout-compatible tensors across distinct TP degrees and reconstructs only incompatible tensors, avoiding a complete second actor copy. On 32 H800 GPUs, Rollplex achieves $1.23\times$--$1.30\times$ speedup over serial colocation and $1.57\times$--$2.24\times$ over disaggregation under the same GPU budget, while preserving the synchronous RL update. Comments: 16 pages, 10 figures Subjects: Machine Learning (cs.LG) ; Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2608.14498 [cs.LG] (or arXiv:2608.14498v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14498 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hanfeng Lu [ view email ] [v1] Fri, 14 Aug 2026 17:13:34 UTC (401 KB) Full-text links: Access Paper: View a PDF of the paper titled Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training, by Hanfeng Lu and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.DC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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