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Nathan Lambert 是 Allen AI 研究所的科学家，博士毕业于加州大学伯克利分校，师从机器人领域的著名学者 Pieter Abbeel。他并非 RLHF 技术的发明者，但他写的《RLHF》这本开源书籍，如今是 AI 从业者理解大模型训练流程的标准参考材料之一。
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The data collected enables strategic decisions about content creation and optimization. If certain queries consistently show competitor sources but never yours, that signals an opportunity to create or improve content addressing that topic. If you're appearing reliably for some questions but not others in the same category, you can analyze what makes your successful content different and apply those lessons to underperforming pieces. If your visibility is declining over time, you know you need to refresh and strengthen your content to maintain AI citation rates.
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Activists working on internet freedom in countries that unjustly criminalize that work