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François Chollet

What François Chollet thinks about world models

@francois-chollet · 17 positions · 2 changes of mind

Everything they publish, on ppll ↗

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We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

6 dated positions, 2025 to 2026, in their own words. Our reading of what François Chollet has said — not written or endorsed by them.

  1. Our readingCoding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop.

    Their wordsCoding isn't yet another application domain -- it's the meta-skill required for AI to automatically develop its own training material, via symbolic world models. That's how the RSI loop actually kicks off.

    ↗@fchollet on Xx.com

    recursive self-improvement

    Interesting
  2. 13 months earlier

  3. Our readingHuman adaptability is fundamentally linked to the efficient construction and refinement of internal world models

    Their wordsWe argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction.

    ↗Assessing Adaptive World Models in Machines with Novel Games (with 13 co-authors)arxiv.org 1st of 5 in this piece

    Interesting

  5. Our readingCurrent AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction

    Their wordsHowever, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment.

    ↗Assessing Adaptive World Models in Machines with Novel Games (with 13 co-authors)arxiv.org 2nd of 5 in this piece

    Interesting

  7. Our readingRapid world model induction will be critical for achieving robust, general AI that functions in complex, fast-changing real and human worlds

    Their wordsWe expect that building and evaluating AI systems capable of this kind of rapid world model induction will be critical for achieving robust, general AI capable of functioning effectively in the complex and fast-changing real world, and especially in human worlds - the environments that human beings have evolved in, created, and are continually changing and re-creating.

    ↗Assessing Adaptive World Models in Machines with Novel Games (with 13 co-authors)arxiv.org 3rd of 5 in this piece

    Interesting

  9. Our readingAn adaptive agent's world model cannot be a static representation learned once and fixed

    Their wordsFor adaptation to complex and changing environments, an agent's world models cannot be static representations learned once and fixed.

    ↗Assessing Adaptive World Models in Machines with Novel Games (with 13 co-authors)arxiv.org 4th of 5 in this piece

    Interesting

  11. Our readingGames are particularly rich and controlled environments uniquely well-suited for evaluating rapid world model induction in AI

    Their wordsWe contend that games provide particularly rich and controlled environments uniquely well-suited for systematically evaluating rapid model adaptation and the process of world model induction.

    ↗Assessing Adaptive World Models in Machines with Novel Games (with 13 co-authors)arxiv.org 5th of 5 in this piece

    Interesting