亚历山大·王（ Alexandr Wang ）透露， Meta首席财务官苏珊·李（ Susan Li ）在夏威夷的一位高管异地将他“多次”拉到一边，警告说，如果模型不起作用，财务状况将非常非常糟糕
扎克伯格： “所以我认为这是现在启动前沿实验室的兴奋感的一部分，基本上就像赌注非常高。”
王： “我认为这是，我有点发自内心地记得这一点。去年年底我们在夏威夷。 这有点像整个高管团队聚在一起。”
“是的， Susan多次把我拉到一边，就像，你知道，我只是想告诉你，如果这不起作用，这将是非常非常糟糕的。 从财务角度来看，这将是非常非常糟糕的。”
“这就像，它必须是好的。 如果它是好的，一切都是好的。我们很棒。但如果它很糟糕，它真的，真的，真的很糟糕。”
扎克伯格： “所以我认为这是现在启动前沿实验室的兴奋感的一部分，基本上就像赌注非常高。”
王： “我认为这是，我有点发自内心地记得这一点。去年年底我们在夏威夷。 这有点像整个高管团队聚在一起。”
“是的， Susan多次把我拉到一边，就像，你知道，我只是想告诉你，如果这不起作用，这将是非常非常糟糕的。 从财务角度来看，这将是非常非常糟糕的。”
“这就像，它必须是好的。 如果它是好的，一切都是好的。我们很棒。但如果它很糟糕，它真的，真的，真的很糟糕。”
查看原文
原帖正文
Alexandr Wang reveals Meta CFO Susan Li pulled him aside "many times" at an exec offsite in Hawaii to warn that if the model didn't work, it would be very, very bad in financial termsZUCKERBERG: "So I think this is part of the excitement of booting up a frontier lab now, where it is basically like the ante is extremely high."WANG: "And I think this is, and I kind of viscerally remember this. We were in Hawaii at the end of last year. It was kind of like the whole exec team got together.""And yeah, Susan pulled me aside many times and was like, you know, I just want to tell you if this doesn't work, this is going to be really, really bad. It's going to be very, very bad in financial terms.""It's like, it has to be good. If it's good, it's all good. We're great. But if it's bad, it's really, really, really bad."
引用自 X
原帖地址：https://x.com/i/status/2109745950810153230
原帖：
本站媒体副本
$ META Devansh Tandon揭示了Meta推荐系统背后的代币输入、参与式飞轮，其中更好的模特将转轴观看时间提升30 ％ ，货币化为下一次培训带来回报“这些缩放曲线不仅仅是学术研究。 他们正在为世界上一些最大的消费者企业推动大规模的实际产品影响。”“以下是我从Meta最近的收入报告中得到的几个例子。 Instagram Reels有一个强劲的季度，同比增长30%。”“我们为提高推荐质量而进行的优化包括简化我们的排名架构，以实现高效的模型缩放和更长的交互历史记录，以确定一个人的兴趣。”“我们将用于训练Instagram的用户交互序列的长度增加了一倍，并提高了每次用户交互的丰富性。”“因此，这些与LLM的幂律缩放曲线直接相似。”“我想介绍一下参与的代币飞轮的想法，这是所有这些RECS模型扩展的动力。”“你训练一个模型，然后在其上运行推理 ，这是其中的代币，该推荐模型产生更好的内容推荐，它推动消费者参与，每日活跃用户花费的时间，它转化为货币化和广告或订阅，为下一次模型训练运行付费。”“因此，缩放曲线上的每一步都是围绕这个飞轮的一个循环，许多消费者应用程序正在将这个核心飞轮作为其业务的核心。 因此，我们在扩展这些推荐系统方面还有很长的路要走。”
_______
有关Meta ADS推荐系统讲座的完整细分： https://firesidealpha.substack.com/p/metas-ad-recommendation-system-and
_______
有关Meta ADS推荐系统讲座的完整细分： https://firesidealpha.substack.com/p/metas-ad-recommendation-system-and
查看引用原文
$META Devansh Tandon reveals the tokens-in, engagement-out flywheel behind Meta's recommendation system, where better models lift Reels watch time 30% and the monetization pays for the next training run"These scaling curves aren't just academic research. They're driving real product impact at scale for some of the biggest consumer businesses in the world.""Here's a couple of examples I have from Meta's recent earnings reports. Instagram Reels had a strong quarter, 30% year on year watch time.""And the optimizations we made to improve the quality of recommendations included simplifying our ranking architecture to enable efficient model scaling and longer interaction histories to identify a person's interests.""We doubled the length of user interaction sequences used for training Instagram and increased the richness of each user interaction.""So these are direct parallels to the power law scaling curves for LLMs.""And I want to introduce this idea of a flywheel of tokens in engagement out, which is what's powering all of these recs models scaling.""You train a model, you then run inference on it, which is the tokens in, that recommendation model results in better content recommendations, it drives consumer engagement, daily active users' time spent, it translates to monetization and ads or subscription, which pays for the next model training run.""And so every step on the scaling curve is one loop around this flywheel, and a lot of consumer apps are spinning this core flywheel at the heart of their business. So we have a long way to scale these recommender systems."
_______
For the full breakdown of the lecture on Meta's ads recommendation system: https://firesidealpha.substack.com/p/metas-ad-recommendation-system-and
_______
For the full breakdown of the lecture on Meta's ads recommendation system: https://firesidealpha.substack.com/p/metas-ad-recommendation-system-and
0 条评论
登录后可以参与讨论。
成员登录还没有评论。第一条认真回应会很重要。