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It seems that you’ve only read the first part of the message. X sometimes aggressively truncates content with no indication it’s done so. I’m not sure this is complete, but I’ve recovered this much:

> I read through these slides and felt like I was transported back to 2018.

> Having been in this spot years ago, thinking about what John & team are thinking about, I can't help but feel like they will learn the same lesson I did the hard way.

> The lesson: on a fundamental level, solutions to these games are low-dimensional. No matter how hard you hit them with from-scratch training, tiny models will work about as well as big ones. Why? Because there's just not that many bits to learn.

> If there's not that many bits to learn, then researcher input becomes non-negligible.

> "I found a trick that makes score go up!" -- yeah, you just hard-coded 100+ bits of information; a winning solution is probably only like 1000 bits. You see progress, but it's not the AI's.

> In this simplified RL setting, you don't see anything close to general intelligence. The neural networks aren't even that important.

> You won't see _real_ learning until you absorb a ton of bits into the model. The only way I really know to do this is with generative modeling.

> A classic example: why is frame stacking just as good as RNNs? John mentioned this in his slides. Shouldn't a better, more general architecture work better?

> YES, it should! But it doesn't, because these environments don't heavily encourage real intelligence.




I'm not sure what the moral is from this, but if Atari games are just too easy, at the same time the response of the machine-learning guys to the challenge of the NetHack Learning Environment seems to have mostly been to quietly give up. Why is generative modeling essential to finding harder challenges when NetHack is right there ...?




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