๐๐๐๐๐ก๐ข๐ง๐ ๐๐๐ง๐๐ ๐ญ๐จ ๐ญ๐ก๐ข๐ง๐ค ๐๐๐๐จ๐ซ๐ ๐ฌ๐ฉ๐๐๐ค๐ข๐ง๐
A recent interview with Eliezer Yudkowsky (known for his work on artificial intelligence safety and rationality) was in mind while reading this summerโs ICML proceedings, and I found STAIR worth sharing. Its key move is to ๐ญ๐ซ๐๐๐ญ ๐ฌ๐๐๐๐ญ๐ฒ ๐๐ฌ ๐ ๐ฌ๐๐๐ซ๐๐ก ๐จ๐ฏ๐๐ซ ๐ซ๐๐๐ฌ๐จ๐ง๐ข๐ง๐ ๐ฉ๐๐ญ๐ก๐ฌ โ the model โthinks before it speaks,โ exploring multiple internal branches and selecting a response that is scored to be both safe and helpful.
This matters because ๐๐๐ง๐๐ ๐ฆ๐จ๐๐๐ฅ๐ฌ ๐๐๐ง ๐ก๐๐ฅ๐ฉ ๐๐๐ ๐๐๐ญ๐จ๐ซ๐ฌ ๐๐จ ๐ญ๐๐ซ๐ซ๐ข๐๐ฅ๐ ๐ญ๐ก๐ข๐ง๐ ๐ฌ; this work is about keeping GenAI models (like Claude, Gemini, and ChatGPT) useful without just saying no to everything and without assisting harmful intent.
Technically speaking, STAIR adds ๐ฌ๐๐๐๐ญ๐ฒ-๐๐ฐ๐๐ซ๐ ๐๐ก๐๐ข๐ง-๐จ๐-๐ญ๐ก๐จ๐ฎ๐ ๐ก๐ญ ๐๐ง๐ ๐ฎ๐ฌ๐๐ฌ ๐๐-๐๐๐๐ (๐๐๐๐๐ญ๐ฒ-๐๐ง๐๐จ๐ซ๐ฆ๐๐ ๐๐จ๐ง๐ญ๐ ๐๐๐ซ๐ฅ๐จ ๐๐ซ๐๐ ๐๐๐๐ซ๐๐ก) ๐ฉ๐ฅ๐ฎ๐ฌ ๐ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ ๐ซ๐๐ฐ๐๐ซ๐ ๐ฆ๐จ๐๐๐ฅ to guide test-time search toward answers that balance safety and helpfulness. That balance matters in, for example, healthcare, education, government, and finance, where the ๐ฅ๐ข๐ง๐๐ฌ ๐๐๐ญ๐ฐ๐๐๐ง ๐ฅ๐๐ ๐ข๐ญ๐ข๐ฆ๐๐ญ๐ ๐๐ง๐ ๐ซ๐ข๐ฌ๐ค๐ฒ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐๐ซ๐ ๐จ๐๐ญ๐๐ง ๐๐ฅ๐ฎ๐ซ๐ซ๐๐. Big labs like Anthropic and OpenAI have pushed RLHF, Constitutional AI, and strong evaluations; STAIR complements that direction by making the ๐ซ๐๐๐ฌ๐จ๐ง๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ ๐ข๐ญ๐ฌ๐๐ฅ๐ ๐ฌ๐๐๐๐ญ๐ฒ-๐๐ฐ๐๐ซ๐ ๐๐ง๐ ๐๐ฒ ๐ฌ๐๐ฅ๐๐๐ญ๐ข๐ง๐ ๐ซ๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐๐ฌ ๐ญ๐ก๐๐ญ ๐ซ๐๐๐ฎ๐๐ ๐ฎ๐ง๐ฌ๐๐๐ ๐จ๐ฎ๐ญ๐ฉ๐ฎ๐ญ๐ฌ ๐ฐ๐ก๐ข๐ฅ๐ ๐ฉ๐ซ๐๐ฌ๐๐ซ๐ฏ๐ข๐ง๐ ๐ก๐๐ฅ๐ฉ๐๐ฎ๐ฅ๐ง๐๐ฌ๐ฌ. I am always eager for more tools that help navigate the tricky balance of alignment!
A link to the original ICML contribution can be found here (Authors: Yichi Zhang ยท Siyuan Zhang ยท Yao Huang ยท Zeyu Xia ยท Zhengwei Fang ยท Xiao Yang ยท Ranjie D. ยท Dong Yan ยท Yinpeng Dong ยท Jun ZHU)