English(EN) When RL Meets Adaptive Speculative Training: A Unified Training-Serving System

Aurora系统统一强化学习训练与服务，加速大语言模型推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了Aurora，一个统一了大型语言模型推测解码训练与服务的新系统。该方法通过持续从实时推理数据中学习，解决了传统离线训练方法相关的延迟和性能下降问题。Aurora集成了基于SGLang的推理服务器和异步强化学习训练服务器，能够立即部署并快速适应变化的流量模式。 AI

影响该系统有望显著降低大语言模型的服务延迟，并提高对新模型和流量变化的适应性。

排序理由这是一篇研究论文，详细介绍了一个用于训练和服务的LLM新系统。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 这是一篇研究论文，详细介绍了一个用于训练和服务的LLM新系统。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Yineng Zhang, Ce Zhang, Tri Dao, Percy Liang, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu · 2026-05-05 04:00

当强化学习遇上自适应推测训练：一个统一的训练-服务系统

arXiv:2602.06932v3 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating speculator training as a standalone offline modeling problem. We show that this dec…