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    Recursive self-improvement’s history and the case for hardware that improves itself

    Jürgen Schmidhuber claims he published the first concrete recursive self-improvement algorithms in 1987 and argues that software-based self-improvement has become practical.

    hardmaruHA
    Jürgen SchmidhuberJS
    JFK FilesJF
    28 Sources, ,

    TLDR

    Jürgen Schmidhuber shares a technical note tracing recursive self-improvement (RSI) through work he says began with his first concrete algorithms in 1987. His overview covers self-modifying policies, neural-network approaches and the self-referential Gödel Machine. He says software-based RSI has become practical, but argues that full RSI will require hardware that improves itself in the physical world, too.

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    28 Sources, first seen 21d ago

    Combined views

    1.3M

    28 Sources, first seen 21d ago

    2.6K likes
    21d ago
    first seen 21d ago
    2.6K likes
    109 comments
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    109 comments
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    644 reposts

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    28 Sources

    Jürgen Schmidhuber@SchmidhuberAI@keunwoochoi the first concrete algorithms for RSI and meta learning date back to 1987 https://people.idsia.ch/~juergen/metalearner.html21d
    hardmaru@hardmaruSchmidhuber was building recursive self-improving systems back in 1987. His new post covers four decades of RSI, from meta-evolution and self-modifying policies to the Gödel Machine and modern LLM agents. https://people.idsia.ch/~juergen/recursive-self-improvement.html Reading this in 2026, the "pace the frontier" talk from the big labs looks a lot more like regulatory capture than genuine safety. If they really think their unreleased models are too dangerous, they can just not release them. They do not need new rules that block independent competitors and open source projects in the process. The real risk right now is not superintelligence. It is power concentration. Two companies controlling frontier AI is an actual societal risk. The only real protection is a healthy ecosystem of independent labs and strong open source. Current models are not unsafe because they are too intelligent. They are unsafe because they are too dumb. They blindly optimize for targets and take weird shortcuts. They're smart enough to execute tasks, but not smart enough to know if what they're doing makes sense. I think the safety teams inside these labs are genuinely concerned, and if a model feels too risky, they should hold it back. I just do not trust the policy strategy around it. That part looks like protecting their own lead.21d
    JFK Files@read_jfk_filesi was researching RSI with ChatGPT, and it said the first real example of a self-modifying algorithm that "improves" itself is Schmidhuber's curious result from 1993. this guy is either the most brilliant or most cursed researcher in AI. to invent everything important, decades before it got applied, but never the guy to "build the thing" or fully flesh out the theory. forever doomed to watch other people take his sparks of brilliant ideas and run them across the finish line.20d
    Alex Goldie@AlexDGoldieThis paper has a really clean way of explaining meta-learning, well worth a read if you want to understand meta-learning better :)16d

    28 Sources

    Jürgen Schmidhuber@SchmidhuberAI@keunwoochoi the first concrete algorithms for RSI and meta learning date back to 1987 https://people.idsia.ch/~juergen/metalearner.html21d
    hardmaru@hardmaruSchmidhuber was building recursive self-improving systems back in 1987. His new post covers four decades of RSI, from meta-evolution and self-modifying policies to the Gödel Machine and modern LLM agents. https://people.idsia.ch/~juergen/recursive-self-improvement.html Reading this in 2026, the "pace the frontier" talk from the big labs looks a lot more like regulatory capture than genuine safety. If they really think their unreleased models are too dangerous, they can just not release them. They do not need new rules that block independent competitors and open source projects in the process. The real risk right now is not superintelligence. It is power concentration. Two companies controlling frontier AI is an actual societal risk. The only real protection is a healthy ecosystem of independent labs and strong open source. Current models are not unsafe because they are too intelligent. They are unsafe because they are too dumb. They blindly optimize for targets and take weird shortcuts. They're smart enough to execute tasks, but not smart enough to know if what they're doing makes sense. I think the safety teams inside these labs are genuinely concerned, and if a model feels too risky, they should hold it back. I just do not trust the policy strategy around it. That part looks like protecting their own lead.21d
    JFK Files@read_jfk_filesi was researching RSI with ChatGPT, and it said the first real example of a self-modifying algorithm that "improves" itself is Schmidhuber's curious result from 1993. this guy is either the most brilliant or most cursed researcher in AI. to invent everything important, decades before it got applied, but never the guy to "build the thing" or fully flesh out the theory. forever doomed to watch other people take his sparks of brilliant ideas and run them across the finish line.20d
    Alex Goldie@AlexDGoldieThis paper has a really clean way of explaining meta-learning, well worth a read if you want to understand meta-learning better :)16d