Tap a claim on the ring to put it at the centre.
← People learn best when the problem in front of them is just past the edge of what they can already do.
17 connected korrents · 16 moments on record from 9 Sept 2020 to 22 Jul 2026.
Everything filed under language learning
language learning
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning 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: People learn best when the problem in front of them is just past the edge of what they can already do. People learn best when the problem in front of them is just past the edge of what they can already do. Last stated 10 months ago
3 Nov 2025
RK
Raph Koster — holds since 2025-11-03 — tap for who they are
Same subject: People persist with the method they enjoy rather than the one that is supposed to be best, and that is what decides the outcome. — tap to centre the map on it People persist with the method they enjoy rather than the one that is supposed to be best, and that is what decides the outcome. Last stated 6 years ago
9 Sept 2020
LL
Luca Lampariello — holds since 2020-09-09 — tap for who they are
Same subject: Pre-training is not the process by which humans learn; it sits somewhere between human learning and human evolution. — tap to centre the map on it Pre-training is not the process by which humans learn; it sits somewhere between human learning and human evolution. Last stated 7 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems. — tap to centre the map on it A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems. Last stated 6 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: Struggling with a hard problem before being shown the answer is not always the best way to learn; a hint or a worked example can beat floundering. — tap to centre the map on it Struggling with a hard problem before being shown the answer is not always the best way to learn; a hint or a worked example can beat floundering. Last stated 2 months ago
22 Jul 2026
SY
Scott H. Young — no longer holds since 2026-07-22 — tap for who they are
Same subject: You should build by solving only the next problem in front of you; most people fail because they look too far ahead. — tap to centre the map on it You should build by solving only the next problem in front of you; most people fail because they look too far ahead. Last stated 2 years ago
20 Aug 2024
PL
Pieter Levels — holds since 2024-08-20 — tap for who they are
Same subject: Any teaching method that helps the weakest students also helps the strongest, so closing the gap between them is not something teaching can do. — tap to centre the map on it Any teaching method that helps the weakest students also helps the strongest, so closing the gap between them is not something teaching can do. Last stated 12 months ago
8 Sept 2025
FD
Freddie deBoer — holds since 2025-09-08 — tap for who they are
Same subject: Faced with a problem none of your techniques fit, try anything, the stupider the better, because the way it fails is the clue. — tap to centre the map on it Faced with a problem none of your techniques fit, try anything, the stupider the better, because the way it fails is the clue. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: To reward the instinct that a new idea is worth having, training would have to score the smallness of the concepts a solution needs, not just whether it solved the problem. — tap to centre the map on it To reward the instinct that a new idea is worth having, training would have to score the smallness of the concepts a solution needs, not just whether it solved the problem. Last stated 2 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: A beginner cannot learn from material made for native speakers: at the start, authentic media is noise. — tap to centre the map on it A beginner cannot learn from material made for native speakers: at the start, authentic media is noise. Last stated 2 months ago
by 20 Jul 2026
SK
Steve Kaufmann — holds since 2026-07-20 — tap for who they are
Same subject: A language is a set of skills to be built, not a body of facts to be known. — tap to centre the map on it A language is a set of skills to be built, not a body of facts to be known. Last stated 5 years ago
19 Aug 2021
LL
Luca Lampariello — holds since 2021-08-19 — tap for who they are
Same subject: A method you hate will end in burnout however well it works for other people, so it should be abandoned rather than endured. — tap to centre the map on it A method you hate will end in burnout however well it works for other people, so it should be abandoned rather than endured. Last stated 6 years ago
9 Sept 2020
LL
Luca Lampariello — holds since 2020-09-09 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — 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 10 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 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — 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 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
At the centre
People learn best when the problem in front of them is just past the edge of what they can already do.
Holds RK Raph KosterRead this korrent →
People persist with the method they enjoy rather than the one that is supposed to be best, and that is what decides the outcome.
Holds LL Luca Lampariello
Pre-training is not the process by which humans learn; it sits somewhere between human learning and human evolution.
Holds DA Dario Amodei
A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems.
Holds TT Terence Tao
Struggling with a hard problem before being shown the answer is not always the best way to learn; a hint or a worked example can beat floundering.
No longer holds SY Scott H. Young
You should build by solving only the next problem in front of you; most people fail because they look too far ahead.
Holds
Any teaching method that helps the weakest students also helps the strongest, so closing the gap between them is not something teaching can do.
Holds FD Freddie deBoer
Faced with a problem none of your techniques fit, try anything, the stupider the better, because the way it fails is the clue.
Holds TT Terence Tao
To reward the instinct that a new idea is worth having, training would have to score the smallness of the concepts a solution needs, not just whether it solved the problem.
Holds GS Grant Sanderson
A beginner cannot learn from material made for native speakers: at the start, authentic media is noise.
Holds SK Steve Kaufmann
A language is a set of skills to be built, not a body of facts to be known.
Holds LL Luca Lampariello
A method you hate will end in burnout however well it works for other people, so it should be abandoned rather than endured.
Holds LL Luca Lampariello
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds DP Dylan Patel
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds JH Jensen Huang
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
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds