alt Hacker NewsThe entire idea of RSI is completely speculative and unproven anyway - the whole underlying claim is that you could prompt a frontier model (at some unspecified level of smarts) to "think about ways to improve your own architecture" and this would then result in the model becoming infinitely smart ("superintelligent") via some sort of foolproof, unconstrained positive feedback. It's more of a science fictiony trope than anything that has been rigorously thought through. People are actually starting to use AI for refining the whole AI serving stack and guess what, this does not result in a sudden superintelligence explosion even though you might technically call it "RSI".
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Yeah but why shouldn't this be possible? We learned that we can already create artifical intelligence that surpasses human intelligence in some dimensions. There is no natural barrier here. The pace of this improvement would be debatable, but what speaks against the possibility of such accelerating self-improvement?
The idea that a few hundred apes with nothing but a bunch of rocks could one day land on the moon and come back to earth safely must’ve sounded ridiculous a hundred thousand years ago
That's not how it works. Look at AlphaEvolve. The model generates hypotheses and designs experiments, and the results of those experiments are fed into the next round, with notable results percolated up to humans for refinement.
Today we prompt software developers to "think about ways to improve AI's architecture" and it results in AI getting better. AI over the last year has made very rapid gains in filling the role of a software developer.
My personal belief, or at least strong hypothesis, is that this kind of recursive self improvement without real world embodied feedback of some kind is impossible.
I think it violates a conservation law. RSI “foom” to superintelligence is an informatic analog to an infinite energy or perpetual motion machine.
To get smarter you must try to solve real problems in the universe and then do some kind of meta learning (natural selection or some other method of refining the intelligence architecture based on an error signal) to iteratively improve your ability to solve real problems. The error signal is outcome measured against a goal function, which for life is survival (probably reducible to genetic fitness and emergent higher order unit fitness from that).
What’s really happening here is learning. To learn, you must have input. You must have training data.
What is the goal function for RSI? Where does the information come from? How do you know if your recursive modifications are making you smarter or just overfitting you to your own idea of smartness?
I predict the latter. RSI will show transient improvement as the current local maximum is optimized and then spiral off into overfitting.
It takes quite a lack of foresight to think RSI is completely speculative when it's already been demonstrated how capable agents are at long horizon tasks given suitable harness and unambiguous success criteria. It's hardly a leap to give LLM the goal of improving itself on benchmarks and let it conduct it's own experiments and spin up training runs completely unsupervised.
It's strange you believe this can't happen when a weaker form of it is already happening. And to be so certain RSI can't happen when there really is no technical basis why it can't.
Yeah, yesterday's talk[1] goes into detail on this, showing how no one really knows how to tackle it because LLMs don't know how to create their own novel objectives.
It's also interesting how many diminishing returns they hit now and how many low hanging fruits are already harvested, it seems like we are approaching the flattening part of the S curve, where further gains become harder to achieve.
1. https://www.youtube.com/watch?v=PrSf7IOYu-I