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English(EN) UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling

UniScale 统一 LLM 路由和缩放，以实现更好的成本-质量权衡

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 UniScale，这是一个统一大型语言模型模型路由和测试时缩放的新颖框架。该方法将自适应推理缩放建模为上下文多臂老虎机问题，通过 LinUCB 学习最优策略。UniScale 旨在通过利用模型路由和测试时缩放的协同作用，在动态推理环境中实现更好的质量-成本权衡，从而克服单独的模型路由和测试时缩放方法的局限性。 AI

影响 通过统一模型路由和测试时缩放来优化 LLM 推理，有可能降低成本并提高性能。

排序理由 该集群包含一篇详细介绍 LLM 推理优化新方法的 isto 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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UniScale 统一 LLM 路由和缩放，以实现更好的成本-质量权衡

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该集群包含一篇详细介绍 LLM 推理优化新方法的 isto 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kaiyu Huang, Xingyu Wang, Mingze Kong, Zhubo Shi, Yuqian Hou, Hong Xu, Zhongxiang Dai, Minchen Yu, Qingjiang Shi ·

    UniScale：通过模型路由和测试时缩放的在线联合优化实现自适应统一推理缩放

    arXiv:2605.30898v1 Announce Type: new Abstract: In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing approaches tackle this trade-off along two largely independent dimensions: model …

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