paper / recommends
Generalized Advantage Estimation
“I highly recommend John Schulman’s “Generalized Advantage Estimation” paper as a good treatment on how to think about various ways to compute it.”
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Eric Jang
Published podcast speaker
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- 20
- Episodes
- 1
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- 1
- Named items
- 1
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What Eric said.
7 transcript-backed records
01 / belief
I think the most profound thing here is that a 10-layer neural network pass, basically 10 steps of reasoning… Of course, the reasoning is not just one trail of thought.
“I think the most profound thing here is that a 10-layer neural network pass, basically 10 steps of reasoning… Of course, the reasoning is not just one trail of thought.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast
02 / belief
I think there’s something quite profound there, and probably underexplored just because Go has been relatively underexplored compared to the boom in LLMs.
“I think there’s something quite profound there, and probably underexplored just because Go has been relatively underexplored compared to the boom in LLMs.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast
03 / belief
I think the question we should be asking ourselves is about how we’ve been formulating solutions to NP-hard problems in worst-case complexity.
“I think the question we should be asking ourselves is about how we’ve been formulating solutions to NP-hard problems in worst-case complexity.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast
04 / belief
I think that’s where a lot of the lateral thinking ability of humans comes from: knowing when the track you’re pursuing or the objective you’re pursuing is not right, and you should be asking a different question.
“I think that’s where a lot of the lateral thinking ability of humans comes from: knowing when the track you’re pursuing or the objective you’re pursuing is not right, and you should be asking a different question.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast
05 / belief
I think there’s a rich library of subtasks and sub-environments that you can train an automated scientist to work on, with Go as a sort of outer verification loop.
“I think there’s a rich library of subtasks and sub-environments that you can train an automated scientist to work on, with Go as a sort of outer verification loop.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast
06 / belief
Certainly, LLMs manage to do something that looks like real human reasoning without having to do an explicit tree structure. That being said, I think the idea of doing forward search and simulation to get a better sense of what is valuable might make a comeback, even if not in exactly the same instantiation as AlphaGo.
“Certainly, LLMs manage to do something that looks like real human reasoning without having to do an explicit tree structure. That being said, I think the idea of doing forward search and simulation to get a better sense of what is valuable might make a comeback, even if not in exactly the same instantiation as AlphaGo.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast
07 / belief
I would say the jury is probably still out on what the final instantiation of reasoning for LLMs will look like.
“I would say the jury is probably still out on what the final instantiation of reasoning for LLMs will look like.”
- Speaker
- Eric Jang
- Publisher
- Dwarkesh Podcast