I wish we had something to bet on better than “inventing a new field of science,”
I’ve thought of one potential observable that is concrete, should be relatively low-capability, and should provoke a strong update towards your model for me:
If there is an AI model such that the complexity of R&D problems it can solve (1) scales basically boundlessly with the amount of serial compute provided to it (or to a “research fleet” based on it), (2) scales much faster with serial compute than with parallel compute, and (3) the required amount of human attention (“babysitting”) is constant or grows very slowly with the amount of serial compute.
This attempts to directly get at the “autonomous self-correction” and “ability to think about R&D problems strategically” ideas.
I’ve not fully thought through all possible ways reality could Goodhart to this benchmark, i. e. “technically” pass it but in a way I find unconvincing. For example, if I failed to include the condition (2), o3 would have probably already “passed” it (since it potentially achieved better performance on ARC-AGI and FrontierMath by sampling thousands of CoTs then outputting the most frequent answer). There might be other loopholes like this...
But it currently seems reasonable and True-Name-y to me.
What about “Daniel Kokotajlo can feed it his docs about some prosaic ML alignment agenda (e.g. the faithful CoT stuff) and then it can autonomously go off and implement the agenda and come back to him with a writeup of the results and takeaways. While working on this, it gets to check in with Daniel once a day for a brief 20-minute chat conversation.”
Does that seem to you like it’ll come earlier, or later, than the milestone you describe?
Prooobably ~simultaneously, but I can maybe see it coming earlier and in a way that isn’t wholly convincing to me. In particular, it would still be a fixed-length task; much longer-length than what the contemporary models can reliably manage today, but still hackable using poorly-generalizing “agency templates” instead of fully general “compact generators of agenty behavior” (which I speculate humans to have and RL’d LLMs not to). It would be some evidence in favor of “AI can accelerate AI R&D”, but not necessarily “LLMs trained via SSL+RL are AGI-complete”.
Actually, I can also see it coming later. For example, some suppose that the capability researchers invent some method for reliably-and-indefinitely extending the amount of serial computations a reasoning model can productively make use of, but the compute or memory requirements grow very fast with the length of a CoT. Some fairly solid empirical evidence and theoretical arguments in favor of boundless scaling can appear quickly, well before the algorithms are made optimal enough to (1) handle weeks-long CoTs and/or (2) allow wide adoption (thus making it available to you).
I think the second scenario is more plausible, actually.
OK. Next question: Suppose that next year we get a nice result showing that there is a model with serial inference-time scaling across e.g. MATH + FrontierMath + IMO problems. Recall that FrontierMath and IMO are subdivided into different difficulty levels; suppose that this model can be given e.g. 10 tokens of CoT, 100, 1000, 10,000, etc. and then somewhere around the billion-serial-token-level it starts solving a decent chunk of the “medium” FrontierMath problems (but not all) and at the million-serial-token level it was only solving MATH + some easy IMO problems.
Not for math benchmarks. Here’s one way it can “cheat” at them: suppose that the CoT would involve the model generating candidate proofs/derivations, then running an internal (learned, not hard-coded) proof verifier on them, and either rejecting the candidate proof and trying to generate a new one, or outputting it. We know that this is possible, since we know that proof verifiers can be compactly specified.
This wouldn’t actually show “agency” and strategic thinking of the kinds that might generalize to open-ended domains and “true” long-horizon tasks. In particular, this would mostly fail the condition (2) from my previous comment.
Something more open-ended and requiring “research taste” would be needed. Maybe a comparable performance on METR’s benchmark would work for this (i. e., the model can beat a significantly larger fraction of it at 1 billion tokens compared to 1 million)? Or some other benchmark that comes closer to evaluating real-world performance.
Edit: Oh, math-benchmark performance would convince me if we get access to a CoT sample and it shows that the model doesn’t follow the above “cheating” approach, but instead approaches the problem strategically (in some sense). (Which would also require this CoT not to be hopelessly steganographied, obviously.)
Have you looked at samples of CoT of o1, o3, deepseek, etc. solving hard math problems? I feel like a few examples have been shown & they seem to involve qualitative thinking, not just brute-force-proof-search (though of course they show lots of failed attempts and backtracking—just like a human thought-chain would).
Anyhow, this is nice, because I do expect that probably something like this milestone will be reached before AGI (though I’m not sure)
Have you looked at samples of CoT of o1, o3, deepseek, etc. solving hard math problems?
Certainly (experimenting with r1′s CoTs right now, in fact). I agree that they’re not doing the brute-force stuff I mentioned; that was just me outlining a scenario in which a system “technically” clears the bar you’d outlined, yet I end up unmoved (I don’t want to end up goalpost-moving).
Though neither are they being “strategic” in the way I expect they’d need to be in order to productively use a billion-token CoT.
Anyhow, this is nice, because I do expect that probably something like this milestone will be reached before AGI
Yeah, I’m also glad to finally have something concrete-ish to watch out for. Thanks for prompting me!
I’ve thought of one potential observable that is concrete, should be relatively low-capability, and should provoke a strong update towards your model for me:
If there is an AI model such that the complexity of R&D problems it can solve (1) scales basically boundlessly with the amount of serial compute provided to it (or to a “research fleet” based on it), (2) scales much faster with serial compute than with parallel compute, and (3) the required amount of human attention (“babysitting”) is constant or grows very slowly with the amount of serial compute.
This attempts to directly get at the “autonomous self-correction” and “ability to think about R&D problems strategically” ideas.
I’ve not fully thought through all possible ways reality could Goodhart to this benchmark, i. e. “technically” pass it but in a way I find unconvincing. For example, if I failed to include the condition (2), o3 would have probably already “passed” it (since it potentially achieved better performance on ARC-AGI and FrontierMath by sampling thousands of CoTs then outputting the most frequent answer). There might be other loopholes like this...
But it currently seems reasonable and True-Name-y to me.
Nice.
What about “Daniel Kokotajlo can feed it his docs about some prosaic ML alignment agenda (e.g. the faithful CoT stuff) and then it can autonomously go off and implement the agenda and come back to him with a writeup of the results and takeaways. While working on this, it gets to check in with Daniel once a day for a brief 20-minute chat conversation.”
Does that seem to you like it’ll come earlier, or later, than the milestone you describe?
Prooobably ~simultaneously, but I can maybe see it coming earlier and in a way that isn’t wholly convincing to me. In particular, it would still be a fixed-length task; much longer-length than what the contemporary models can reliably manage today, but still hackable using poorly-generalizing “agency templates” instead of fully general “compact generators of agenty behavior” (which I speculate humans to have and RL’d LLMs not to). It would be some evidence in favor of “AI can accelerate AI R&D”, but not necessarily “LLMs trained via SSL+RL are AGI-complete”.
Actually, I can also see it coming later. For example, some suppose that the capability researchers invent some method for reliably-and-indefinitely extending the amount of serial computations a reasoning model can productively make use of, but the compute or memory requirements grow very fast with the length of a CoT. Some fairly solid empirical evidence and theoretical arguments in favor of boundless scaling can appear quickly, well before the algorithms are made optimal enough to (1) handle weeks-long CoTs and/or (2) allow wide adoption (thus making it available to you).
I think the second scenario is more plausible, actually.
OK. Next question: Suppose that next year we get a nice result showing that there is a model with serial inference-time scaling across e.g. MATH + FrontierMath + IMO problems. Recall that FrontierMath and IMO are subdivided into different difficulty levels; suppose that this model can be given e.g. 10 tokens of CoT, 100, 1000, 10,000, etc. and then somewhere around the billion-serial-token-level it starts solving a decent chunk of the “medium” FrontierMath problems (but not all) and at the million-serial-token level it was only solving MATH + some easy IMO problems.
Would this count, for you?
Not for math benchmarks. Here’s one way it can “cheat” at them: suppose that the CoT would involve the model generating candidate proofs/derivations, then running an internal (learned, not hard-coded) proof verifier on them, and either rejecting the candidate proof and trying to generate a new one, or outputting it. We know that this is possible, since we know that proof verifiers can be compactly specified.
This wouldn’t actually show “agency” and strategic thinking of the kinds that might generalize to open-ended domains and “true” long-horizon tasks. In particular, this would mostly fail the condition (2) from my previous comment.
Something more open-ended and requiring “research taste” would be needed. Maybe a comparable performance on METR’s benchmark would work for this (i. e., the model can beat a significantly larger fraction of it at 1 billion tokens compared to 1 million)? Or some other benchmark that comes closer to evaluating real-world performance.
Edit: Oh, math-benchmark performance would convince me if we get access to a CoT sample and it shows that the model doesn’t follow the above “cheating” approach, but instead approaches the problem strategically (in some sense). (Which would also require this CoT not to be hopelessly steganographied, obviously.)
Have you looked at samples of CoT of o1, o3, deepseek, etc. solving hard math problems? I feel like a few examples have been shown & they seem to involve qualitative thinking, not just brute-force-proof-search (though of course they show lots of failed attempts and backtracking—just like a human thought-chain would).
Anyhow, this is nice, because I do expect that probably something like this milestone will be reached before AGI (though I’m not sure)
Certainly (experimenting with r1′s CoTs right now, in fact). I agree that they’re not doing the brute-force stuff I mentioned; that was just me outlining a scenario in which a system “technically” clears the bar you’d outlined, yet I end up unmoved (I don’t want to end up goalpost-moving).
Though neither are they being “strategic” in the way I expect they’d need to be in order to productively use a billion-token CoT.
Yeah, I’m also glad to finally have something concrete-ish to watch out for. Thanks for prompting me!