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← Scaling up GPT will never produce AGI, because a model trained on…
17 connected korrents · 13 moments from 24 Feb 2018 to 13 Sept 2026. Nearly all of them are about reinforcement learning .
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scaling laws Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Scaling up GPT will never produce AGI, because a model trained on cross-entropy loss cannot get there; reinforcement learning in rich environments is required. Scaling up GPT will never produce AGI, because a model trained on cross-entropy loss cannot get there; reinforcement learning in rich environments is required. Last stated 3 years ago
29 Jun 2023
GH
George Hotz — holds since 2023-06-29 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. — tap to centre the map on it Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. Last stated 7 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Current models are unsafe because they are not smart enough, not because they are too smart. — tap to centre the map on it Current models are unsafe because they are not smart enough, not because they are too smart. Last stated a month ago
13 Sept 2026
FC
François Chollet — holds since 2026-09-13 — tap for who they are
Same subject: Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. — tap to centre the map on it Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. Last stated a year ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. — tap to centre the map on it Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — tap for who they are
Same subject: It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning. — tap to centre the map on it It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning. Last stated 2 years ago
28 Nov 2024
LW
Lilian Weng — holds since 2024-11-28 — tap for who they are
Same subject: Large language models mimic what people say to do rather than work out what to do, which is why they are not about understanding the world. — tap to centre the map on it Large language models mimic what people say to do rather than work out what to do, which is why they are not about understanding the world. Last stated a year ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments. — tap to centre the map on it Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated. — tap to centre the map on it A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated. Last stated 5 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: A poor country should prioritise owning a piece of AI over retraining its workers, but it should not bet everything on that. — tap to centre the map on it A poor country should prioritise owning a piece of AI over retraining its workers, but it should not bet everything on that. Last stated 5 months ago
4 Jun 2026
PT
Phil Trammell — holds since 2026-06-04 — tap for who they are
Same subject: A slow takeoff is significantly more likely than a fast one: AI that is nearly as powerful will have transformed the world before the incredibly powerful kind arrives. — tap to centre the map on it A slow takeoff is significantly more likely than a fast one: AI that is nearly as powerful will have transformed the world before the incredibly powerful kind arrives. Last stated 9 years ago
24 Feb 2018
PC
Paul Christiano — holds since 2018-02-24 — tap for who they are
Same subject: The gap between chimps and humans is no evidence that a small increase in compute yields an explosion of capability, because evolution was not optimising chimps for technology. — tap to centre the map on it The gap between chimps and humans is no evidence that a small increase in compute yields an explosion of capability, because evolution was not optimising chimps for technology. Last stated 9 years ago
24 Feb 2018
PC
Paul Christiano — holds since 2018-02-24 — tap for who they are
Same subject: There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck. — tap to centre the map on it There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: No company should be making the decisions about AGI, and no one person should have super-voting control of one; governments have to put rules of the road in place. — tap to centre the map on it No company should be making the decisions about AGI, and no one person should have super-voting control of one; governments have to put rules of the road in place. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Superhuman persuasion will arrive before superhuman intelligence, which means whoever builds a model can embed their ideals in it long before anyone reaches AGI. — tap to centre the map on it Superhuman persuasion will arrive before superhuman intelligence, which means whoever builds a model can embed their ideals in it long before anyone reaches AGI. Last stated 2 years ago
3 Feb 2025
DP
Dylan Patel — holds since 2025-02-03 — tap for who they are
Same subject: The board of a nonprofit answers to no one but itself, and the board governing the road to AGI ought instead to answer to the world as a whole. — tap to centre the map on it The board of a nonprofit answers to no one but itself, and the board governing the road to AGI ought instead to answer to the world as a whole. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
At the centre
Scaling up GPT will never produce AGI, because a model trained on cross-entropy loss cannot get there; reinforcement learning in rich environments is required.
Holds Read this korrent →
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago.
Holds
Current models are unsafe because they are not smart enough, not because they are too smart.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one.
Holds
It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning.
Holds
Large language models mimic what people say to do rather than work out what to do, which is why they are not about understanding the world.
Holds
Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments.
Holds
A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated.
Holds
A poor country should prioritise owning a piece of AI over retraining its workers, but it should not bet everything on that.
Holds
A slow takeoff is significantly more likely than a fast one: AI that is nearly as powerful will have transformed the world before the incredibly powerful kind arrives.
Holds
The gap between chimps and humans is no evidence that a small increase in compute yields an explosion of capability, because evolution was not optimising chimps for technology.
Holds
There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds
No company should be making the decisions about AGI, and no one person should have super-voting control of one; governments have to put rules of the road in place.
Holds
Superhuman persuasion will arrive before superhuman intelligence, which means whoever builds a model can embed their ideals in it long before anyone reaches AGI.
Holds
The board of a nonprofit answers to no one but itself, and the board governing the road to AGI ought instead to answer to the world as a whole.
Holds