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I wonder why Gemini used RLHF instead of Direct Preference Optimization (DPO). DPO was written up 6 months ago; it's simpler and apparently more compute-efficient than RLHF.

  • Is the Gemini org structure so sclerotic that it couldn't switch to a more efficient training algorithm partway through a project?
  • Is DPO inferior to RLHF in some way? Lower quality, less efficient, more sensitive to hyperparameters?
  • Maybe they did use DPO, even though they claimed it was RLHF in their technical report?

Thanks! For convex sets of distributions: If you weaken the definition of fixed point to , then the set has a least element which really is a least fixed point.

Conception is a startup trying to do in vitro gametogenesis for humans!

CFAR used to have an awesome class called "Be specific!" that was mostly about concreteness. Exercises included:

  • Rationalist taboo
  • A group version of rationalist taboo where an instructor holds an everyday object and asks the class to describe it in concrete terms.
  • The Monday-Tuesday game
  • A role-playing game where the instructor plays a management consultant whose advice is impressive-sounding but contentless bullshit, and where the class has to force the consultant to be specific and concrete enough to be either wrong or trivial.
  • People were encouraged to make a habit of saying "can you give an example?" in everyday conversation. I practiced it a lot.

IIRC, Eliezer taught the class in May 2012? He talks about the relevant skills here and here. And then I ran it a few times, and then CFAR dropped it; I don't remember why.

Yep, I skimmed it by looking at the colorful plots that look like Ising models and reading the captions. Those are always fun.

No, I just took a look. The spin glass stuff looks interesting!

I think you're saying , right? In that case, since embeds into , we'd have embedding into . So not really a step up.

If you want to play ordinal games, you could drop the requirement that agents are computable / Scott-continuous. Then you get the whole ordinal hierarchy. But then we aren't guaranteed equilibria in games between agents of the same order.

I suppose you could have a hybrid approach: Order is allowed to be discontinuous in its order- beliefs, but higher orders have to be continuous? Maybe that would get you to .

I apologize, I shouldn't have leapt to that conclusion.

it legitimately takes the whole 4 years after that to develop real AGI that ends the world. FINE. SO WHAT. EVERYONE STILL DIES.

By Gricean implicature, "everyone still dies" is relevant to the post's thesis. Which implies that the post's thesis is that humanity will not go extinct. But the post is about the rate of AI progress, not human extinction.

This seems like a bucket error, where "will takeoff be fast or slow?" and "will AI cause human extinction?" are put in the same bucket.

The central hypothesis of "takeoff speeds" is that at the time of serious AGI being developed, it is perfectly anti-Thielian in that it is devoid of secrets

No, the slow takeoff model just precludes there being one big secret that unlocks both 30%/year growth and dyson spheres. It's totally compatible with a bunch of medium-sized $1B secrets that different actors discover, adding up to hyperbolic economic growth in the years leading up to "rising out of the atmosphere".

Rounding off the slow takeoff hypothesis to "lots and lots of little innovations adding up to every key AGI threshold, which lots of actors are investing $10 million in at a time" seems like black-and-white thinking, demanding that the future either be perfectly Thielien or perfectly anti-Thielien. The real question is a quantitative one — how lumpy will takeoff be?

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