Unverified50% confidenceFactExact time
RL训练涉及环境交互（CPU密集）和策略优化（GPU密集）两个阶段的交替
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
强化学习算力优化实战：提升CPU与GPU利用率的核心策略
redditr/reinforcementlearning7/10/2026
Related Claims
UnverifiedRL训练循环包含Rollout（环境交互，CPU密集）和Learning（网络更新，GPU密集）两个阶段，常串行执行导致算力浪费81% similarUnverified后训练优化包括指令微调、RLHF或DPO策略调整、系统提示词工程改进及工具调用和多模态融合能力强化73% similarUnverified系统提示词优先级高于用户指令是通过RLHF阶段专门的对齐训练数据刻意强化的71% similarUnverified将RL工作流集成到NeMo可减少训练-部署鸿沟带来的性能损耗，因训练与部署环境高度对齐71% similarUnverified混合精度训练和梯度累积等技术可以在有限GPU内存下训练更大模型70% similar
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