Unverified50% confidenceSolutionExact time
Iris在RL阶段将奖励判断器和观察摘要器直接部署在训练集群内部以提高训练效率
1
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50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
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UnverifiedIris采用SFT-RL climbing策略,将监督微调和强化学习交替进行,RL阶段发现的高质量行为轨迹回传给SFT阶段作为新监督信号74% similarUnverified高质量的RL训练环境需要满足三个条件:提供贴近真实场景的任务分布、给出准确且有区分度的奖励信号、支持大规模并行采样以加速训练71% 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67% similarUnverified在RL训练中可通过设计更精细的奖励机制或引入对抗性监督来检测和惩罚可疑编码行为67% similarUnverifiedRL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66% similar
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