Aurora：让大模型边干边学

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Aurora 是一个将大语言模型训练与推理深度融合的创新系统，旨在解决投机采样技术中模型训练与实际服务脱节的难题。该系统通过异步强化学习机制，直接从实时推理轨迹中持续学习，实现了投机模型在上线首日的即时加速与自动优化。它不仅支持热插拔更新以确保服务不间断，还利用树状注意力机制高效处理接受和拒绝的令牌反馈。实验表明，Aurora 能显著提升推理吞吐量，并能迅速适应用户请求分布的变化。这种“边服务边训练”的模式打破了传统预训练的限制，极大地降低了基础设施成本并消除了部署延迟。

Episode metadata supplied by the publisher feed · Published Apr 19, 2026

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Aurora：让大模型边干边学

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