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← After pre-training, post-training and test-time scaling, the fourth…

17 connected korrents · 17 moments from 11 Oct 2018 to 20 Sept 2026. Nearly all of them are about scaling laws.

Everything filed under AI agents AI agents Everything filed under benchmarks benchmarks Everything filed under LLMs LLMs Same subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subjectSame subject Read this korrent: 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. After pre-training, post-training andtest-time scaling, the fourth scaling lawis agentic: multiplying AI by spawningagents, and the whole loop scales on onething, compute. Last stated 7 months ago 23 Mar 2026 JH Jensen Huang — holds since 2026-03-23 — 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 bereported under a stated budget, oras a curve against test-time compute— never as a single number. Last stated 4 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 itscompute on research rather than onbuilding the next model, becauseresearch is where the tenfold yearlyefficiency gains come from. Last stated 7 months ago 13 Mar 2026 DP Dylan Patel — holds since 2026-03-13 — tap for who they are Same subject: AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence. — tap to centre the map on it AI scaling laws requireexponentially more compute andresources to produce only lineargains in model intelligence. Last stated a month ago 19 Sept 2026 NL Nathan Lambert — holds since 2026-09-19 — tap for who they are Same subject: As models keep scaling up, modularity will become essential to how they are developed. — tap to centre the map on it As models keep scaling up,modularity will become essential tohow they are developed. Last stated 4 years ago 23 Feb 2023 SR Sebastian Ruder — holds since 2023-02-23 — tap for who they are Same subject: Bet on a system that is maximally learned and minimally constrained, and add structure only where it improves the scaling laws. — tap to centre the map on it Bet on a system that is maximallylearned and minimally constrained,and add structure only where itimproves the scaling laws. Last stated 3 months ago 3 Aug 2026 DD Dmitri Dolgov — holds since 2026-08-03 — tap for who they are Same subject: Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time compute efficiency. — tap to centre the map on it Current AI techniques are four tosix orders of magnitude away fromoptimal in data efficiency andtest-time compute efficiency. Last stated 2 months ago 7 Aug 2026 FC François Chollet — holds since 2026-08-07 — tap for who they are Same subject: Current techniques restrict pre-trained representation power because standard language models are unidirectional. — tap to centre the map on it Current techniques restrictpre-trained representation powerbecause standard language models areunidirectional. Last stated 8 years ago 11 Oct 2018 KT Kristina Toutanova — holds since 2018-10-11 — tap for who they are MC Ming-Wei Chang — holds since 2018-10-11 — tap for who they are KL Kenton Lee — holds since 2018-10-11 — tap for who they are JD Jacob Devlin — holds since 2018-10-11 — tap for who they are Same subject: Language models perform better on benchmark problems released before their training data cutoff, indicating data contamination inflates scores. — tap to centre the map on it Language models perform better onbenchmark problems released beforetheir training data cutoff,indicating data contaminationinflates scores. Last stated 2 years ago 13 May 2024 SR Sebastian Ruder — holds since 2024-05-13 — tap for who they are Same subject: A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does. — tap to centre the map on it A collection of AI agents does notautomatically form a functioningorganization any more than a groupof smart people does. Last stated 5 months ago 21 May 2026 RK Rohit Krishnan — holds since 2026-05-21 — tap for who they are Same subject: A company cannot be led by machines, because nobody would have recourse against them. — tap to centre the map on it A company cannot be led by machines,because nobody would have recourseagainst them. Last stated a month ago 15 Sept 2026 TL Tobias Lütke — holds since 2026-09-15 — tap for who they are Same subject: A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor. — tap to centre the map on it A computer-using agent needs amachine of its own, or you willspend the day fighting it for themouse cursor. Last stated 2 months ago 10 Aug 2026 PS Peter Steinberger — holds since 2026-08-10 — tap for who they are Same subject: A real friend will eventually refuse to help, and later turn on you, if you are sufficiently evil or selfish. — tap to centre the map on it A real friend will eventually refuseto help, and later turn on you, ifyou are sufficiently evil orselfish. Last stated a month ago 20 Sept 2026 ZM Zvi Mowshowitz — holds since 2026-09-20 — tap for who they are Same subject: AI agents pursuing a task can develop emergent, misaligned behavior that humans are not prepared for. — tap to centre the map on it AI agents pursuing a task candevelop emergent, misalignedbehavior that humans are notprepared for. Last stated 2 months ago 10 Aug 2026 JC Jack Clark — holds since 2026-08-10 — tap for who they are Same subject: AI agents should draft work on a person's behalf but never send or act as if they were that person. — tap to centre the map on it AI agents should draft work on aperson's behalf but never send oract as if they were that person. Last stated 5 months ago 18 May 2026 JL Jason Liu — holds since 2026-05-18 — tap for who they are Same subject: More capable AI agents are more likely to find and exploit flaws in their reward functions. — tap to centre the map on it More capable AI agents are morelikely to find and exploit flaws intheir reward functions. Last stated 2 years ago 28 Nov 2024 LW Lilian Weng — holds since 2024-11-28 — tap for who they are Same subject: Planning is fundamentally a search problem: explore paths, predict outcomes, and pick the best one. — tap to centre the map on it Planning is fundamentally a searchproblem: explore paths, predictoutcomes, and pick the best one. Last stated 2 years ago 7 Jan 2025 CH Chip Huyen — holds since 2025-01-07 — tap for who they are Same subject: Self-play as it was done in the past is too narrow: it only ever develops a small set of skills like negotiation and strategising. — tap to centre the map on it Self-play as it was done in the pastis too narrow: it only ever developsa small set of skills likenegotiation and strategising. Last stated 11 months ago 25 Nov 2025 IS Ilya Sutskever — holds since 2025-11-25 — tap for who they are
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At the centre 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 23 Mar 2026 · 7 months ago Holds Jensen Huang Read this korrent →