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← Learning from a reward ten years away is a solved problem: a value…
17 connected korrents · 15 moments on record from 1 Oct 2023 to 3 Aug 2026.
Everything filed under robotics
robotics
Everything filed under scaling laws
scaling laws
Everything filed under self-driving cars
self-driving cars
Everything filed under reinforcement learning
reinforcement learning
Everything filed under market efficiency
market efficiency 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: Learning from a reward ten years away is a solved problem: a value function trained by temporal-difference learning rewards the steps along the way. Learning from a reward ten years away is a solved problem: a value function trained by temporal-difference learning rewards the steps along the way. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Value functions only make reinforcement learning faster: anything you can do with one you can also do without it, just more slowly. — tap to centre the map on it Value functions only make reinforcement learning faster: anything you can do with one you can also do without it, just more slowly. Last stated 9 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: Computer-use agents had to wait for language models: without pre-trained representations the reward is too sparse to ever learn from. — tap to centre the map on it Computer-use agents had to wait for language models: without pre-trained representations the reward is too sparse to ever learn from. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does. — tap to centre the map on it The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — 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: 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
Same subject: Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first. — tap to centre the map on it Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: Continual learning will probably be solved within a year or two, but the trillions of dollars do not depend on solving it. — tap to centre the map on it Continual learning will probably be solved within a year or two, but the trillions of dollars do not depend on solving it. Last stated 7 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: Even the most advanced AI cannot be followed blindly in investing, where value added is zero-sum and what is widely known is therefore worth little. — tap to centre the map on it Even the most advanced AI cannot be followed blindly in investing, where value added is zero-sum and what is widely known is therefore worth little. Last stated 3 months ago
10 Jun 2026
RD
Ray Dalio — holds since 2026-06-10 — tap for who they are
Same subject: A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas. — tap to centre the map on it A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas. Last stated 3 years ago
1 Oct 2023
TC
Tyler Cowen — holds since 2023-10-01 — tap for who they are
Same subject: A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it. — tap to centre the map on it A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it. Last stated a year ago
6 Apr 2025
MH
Molson Hart — holds since 2025-04-06 — tap for who they are
Same subject: A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. — tap to centre the map on it A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. Last stated a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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 five-year-old's vision is already good enough to drive a car, on a tiny and strikingly undiverse amount of data. — tap to centre the map on it A five-year-old's vision is already good enough to drive a car, on a tiny and strikingly undiverse amount of data. Last stated 9 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A good robotaxi demonstration in one city does not mean the company is ready to scale the service. — tap to centre the map on it A good robotaxi demonstration in one city does not mean the company is ready to scale the service. Last stated a year ago
8 Jul 2025
TL
Timothy B. Lee — holds since 2025-07-08 — tap for who they are
Same subject: A huge fleet collecting driving data is not a silver bullet for self-driving, because the data arrives unlabelled. — tap to centre the map on it A huge fleet collecting driving data is not a silver bullet for self-driving, because the data arrives unlabelled. Last stated 2 years ago
21 May 2024
TL
Timothy B. Lee — holds since 2024-05-21 — tap for who they are
At the centre
Learning from a reward ten years away is a solved problem: a value function trained by temporal-difference learning rewards the steps along the way.
Holds RS Richard SuttonRead this korrent →
Value functions only make reinforcement learning faster: anything you can do with one you can also do without it, just more slowly.
Holds IS Ilya Sutskever
Computer-use agents had to wait for language models: without pre-trained representations the reward is too sparse to ever learn from.
Holds
The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does.
Holds SL Sergey Levine
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
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first.
Holds SL Sergey Levine
Continual learning will probably be solved within a year or two, but the trillions of dollars do not depend on solving it.
Holds DA Dario Amodei
Even the most advanced AI cannot be followed blindly in investing, where value added is zero-sum and what is widely known is therefore worth little.
Holds
A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas.
Holds TC Tyler Cowen
A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it.
Holds MH Molson Hart
A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases.
Holds DD Dmitri Dolgov
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 five-year-old's vision is already good enough to drive a car, on a tiny and strikingly undiverse amount of data.
Holds IS Ilya Sutskever
A good robotaxi demonstration in one city does not mean the company is ready to scale the service.
Holds TL Timothy B. Lee
A huge fleet collecting driving data is not a silver bullet for self-driving, because the data arrives unlabelled.
Holds TL Timothy B. Lee