[stormfather]

I wasn't clear. Model weights aren't changing at inference time. I meant at inference time the model will output a sequence of thoughts and actions to perform tasks given to it by the user. For instance, to answer a question it will search the web, navigate through some sites, scroll, summarize, etc. You can model this as a game played by emitting a sequence of actions in a browser. RL is the technique you want to train this component. To scale this up you need to have a massive amount of examples of sequences of actions taken in the browser, the outcome it led to, and a label for if that outcome was desirable or not. I am saying that by recording users googling stuff and emailing each other for decades Google has this massive dataset to train their RL powered browser using agent. Deepseek proving that simple RL ca be cheaply applied to a frontier LLM and have reasoning organically emerge makes this approach more obviously viable.

[shwaj]

Makes sense, thanks. I wonder whether human web-browsing strategies are optimal for use in a LLM, e.g. given how much faster LLMs are at reading the webpages they find, compared to humans? Regardless, it does seem likely that Google’s dataset is good for something.

[stormfather]

Take this example:

A human googles "how much does a tire cost?"

They pick out a website from search results, then nav within it to the correct product page and maybe scroll until the price is visible on screen.

Google captures a lot of that data on third party sites. From Perplexity:

Google Analytics: If the website uses Google Analytics, Google can collect data about user behavior on that site, including page views, time on site, and user flow.

Google Ads: Websites using Google Ads may allow Google to track user interactions for ad targeting and conversion tracking.

Other Google Services: Sites implementing services like Google Tag Manager or using embedded YouTube videos may provide additional tracking opportunities

So you can imagine that Google has a kajillion training examples that go: search query (which implies task) -> pick webpage -> actions within webpage -> user stops (success), or user backs off site/tries different query (failure)

You can imagine that even if an AI agent is super efficient, it still needs to learn how to formulate queries, pick out a site to visit, nav through the site, do all that same stuff to perform tasks. Google's dataset is perfect for this, huge, and unparalleled.