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Life sciences · Preprint
arXiv · August 14, 2026
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Rollplex is a GPU memory and scheduling optimization for vision-language model RL post-training that overlaps prefix computation across RL phases. The work reports 1.23×–1.30× speedup over serial execution and 1.57×–2.24× over disaggregation on a 32-GPU cluster, but is an unreviewed engineering contribution with no validation of whether the speedup improves downstream task performance or generalizes beyond the tested hardware and model.
Preprint. Intervention: Rollplex runtime: phase-aware memory management and parallelism-aware weight sharing to overlap prefix computation across RL rollout, reference scoring, and training phases.. Compared with: Serial colocation and disaggregated execution baselines on the same 32-GPU cluster..
Rollplex achieves 1.23×–1.30× speedup over serial colocation on 32 H800 GPUs Rollplex achieves 1.57×–2.24× speedup over disaggregation under the same GPU budget Phase-aware memory management and parallelism-aware weight sharing enable concurrent rollout and training phases without breaking synchronous RL semantics
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This is an unreviewed systems engineering paper demonstrating a runtime optimization technique with reported speedup metrics, but lacks clinical or scientific validation, ground-truth comparison, or peer review.
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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.
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