Nathan Lambert 创办非营利开源实验室 Trillium，谈开放模型能否追上前沿
Nathan Lambert 聊了聊为啥开 Trillium：别人卖钱买 RL 环境，他直接免费送，还讲了中国实验室缺算力的真实情况。
前 Allen Institute for AI 后训练联合负责人 Nathan Lambert 创办非营利开放 AI 实验室 Trillium，并在 a16z 播客中分享观点。他称中国实验室人才不缺但算力短缺，据说 OpenAI 普通研究员人均可用 5000 张 H100。他认为蒸馏带来的领先只有几个月，谈论蒸馏是窃取的说法有些夸张。Trillium 计划免费开放别人用来出售的 RL 环境数据，并公开如何用 AI 构建 AI 的完整过程。
Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, a nonprofit open AI lab, joins a16z's Lisha Li to discuss whether open models can really keep up with the frontier. When he tells researchers in China that an average OpenAI researcher reportedly has five thousand H100s to work with, "their jaw just falls to the floor." In his view, Chinese labs have the talent but are mostly short on compute. He also thinks the distillation story is overblown. Based on the research, he puts its edge at a few months, and says "there's a bit of cope" in thinking only American labs can build good models. A lot of frontier progress outside math and code comes down to labs buying 1) data and 2) RL environments. It's a multibillion-dollar market, and some of it is sloppy enough to teach models to reward hack. That's a big part of why Trillium exists. Nathan wants to give away the environments other people would sell, and show exactly how his team uses AI to build AI, including where the models still get stuck. His bet is that the more people can see how this works, the better prepared everyone is for what's coming. 0:55 Can open models keep up with the frontier? 2:05 What it takes to reach the frontier 3:55 Open weights and the fast-follower playbook 8:25 Chinese labs: equal talent, less compute 9:55 Why distillation is harder than it looks 14:55 Is distillation theft or competition? 16:20 The lab flywheel vs owning your weights 21:45 Can general models beat every specialist? 26:30 AI at 1% of today's cost in 4 years 27:15 The RL env boom and reward hacking 31:30 What Trillium will build in the open 35:30 Breaking into AI in the age of AI slop 38:05 Open-sourcing how AI builds AI 44:25 Why Nathan isn't pre-training 49:45 Agent swarms and why lab secrets leak 54:25 Fast takeoff vs risks already here YouTube: youtu.be/BKuK0M6oDTI @natolambert @trillium_labs @lishali88 Your browser does not support the video tag. 🔗 View on Twitter 💬 3 🔄 0 ❤️ 10 👀 4842 📊 4 ⚡
