Can Wikipedia Help Offline Reinforcement Learning? · HackerPapers discussions

Can Wikipedia Help Offline Reinforcement Learning? — mentioned by ag8

Wed, 17 Sep 2025 18:51:14 GMT

Yeah, for better or worse, the way the median startup interfaces with AI these days is through an LLM API, and that's what all the workflows are built around, so that's what we're targeting. Though, depending on what you're trying to do, I wouldn't discount the use of starting with a pretrained model—there was that famous result from 2022 that showed that pretraining a model on _Wikipedia_ made training on Atari games more than twice as efficient [0]; these days, LLMs have huge amounts of priors about the real world that make them great starting points for a surprisingly diverse set of tasks…

Paper and discussion history. A mention is not an endorsement.

Can Wikipedia Help Offline Reinforcement Learning? — mentioned by gwern

Tue, 14 Jun 2022 17:27:53 GMT

Because Nethack and video games remove most of the reasons that GOFAI failed; for example, everything hard about perception from pixels is removed when you have a little parsed text grid of cells presented to you or are hooked into the game engine and query objects directly. (The real world is not made of a clean little grid of discrete high-level objects like 'dragon' or 'black jelly'.) Moravec's paradox again: humans find perception easy but things like Sokoban hard, while GOFAI approaches find Sokoban so trivial that it's common to use it (or Sudoku) as a toy problem introduction to…

Paper and discussion history. A mention is not an endorsement.