研究人员开发了一种系统集成的推测解码方法，以加速大型语言模型的训练后部署生成。该技术在 NeMo-RL 中使用 vLLM 后端实现，作为一种无损加速原语，可保持目标模型的输出分布。在 8B 模型上的初步测试显示，部署吞吐量提高了 1.8 倍，模拟预测使用异步 RL 管道对更大模型可实现高达 2.5 倍的加速。 AI
影响 加速 LLM 训练速度，可能降低新模型的计算成本和部署时间。
排序理由 学术论文，详细介绍了一种加速 LLM 训练的新方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2604.26779v1 Announce Type: cross Abstract: RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central systems challenge. Many existing efficiency methods improve throughput by changi…
RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central systems challenge. Many existing efficiency methods improve throughput by changing the rollout or optimization regime, for example…
RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central systems challenge. Many existing efficiency methods improve throughput by changing the rollout or optimization regime, for example…