Rant on Problem Factorization for Alignment

This post is the second in what is likely to become a series of uncharitable rants about alignment proposals (previously: Godzilla Strategies). In general, these posts are intended to convey my underlying intuitions. They are not intended to convey my all-things-considered, reflectively-endorsed opinions. In particular, my all-things-considered reflectively-endorsed opinions are usually more kind. But I think it is valuable to make the underlying, not-particularly-kind intuitions publicly-visible, so people can debate underlying generators directly. I apologize in advance to all the people I insult in the process.

With that in mind, let’s talk about problem factorization (a.k.a. task decomposition).


It all started with HCH, a.k.a. The Infinite Bureaucracy.

The idea of The Infinite Bureaucracy is that a human (or, in practice, human-mimicking AI) is given a problem. They only have a small amount of time to think about it and research it, but they can delegate subproblems to their underlings. The underlings likewise each have only a small amount of time, but can further delegate to their underlings, and so on down the infinite tree. So long as the humans near the top of the tree can “factorize the problem” into small, manageable pieces, the underlings should be able to get it done. (In practice, this would be implemented by training a question-answerer AI which can pass subquestions to copies of itself.)

At this point the ghost of George Orwell chimes in, not to say anything in particular, but just to scream. The ghost has a point: how on earth does an infinite bureaucracy seem like anything besides a terrible idea?

“Well,” says a proponent of the Infinite Bureaucracy, “unlike in a real bureaucracy, all the humans in the infinite bureaucracy are actually just trying to help you, rather than e.g. engaging in departmental politics.” So, ok, apparently this person has not met a lot of real-life bureaucrats. The large majority are decent people who are honestly trying to help. It is true that departmental politics are a big issue in bureaucracies, but those selection pressures apply regardless of the peoples’ intentions. And also, man, it sure does seem like Coordination is a Scarce Resource and Interfaces are a Scarce Resource and scarcity of those sorts of things sure would make bureaucracies incompetent in basically the ways bureacracies are incompetent in practice.

Debate and Other Successors

So, ok, maybe The Infinite Bureaucracy is not the right human institution to mimic. What institution can use humans to produce accurate and sensible answers to questions, robustly and reliably? Oh, I know! How about the Extremely Long Jury Trial? Y’know, because juries are, in practice, known for their extremely high reliability in producing accurate and sensible judgements!

The Magic 8 Ball, known for being about as reliable as a Jury Trial.

“Well,” says the imaginary proponent, “unlike in a real Jury Trial, in the Extremely Long Jury Trial, the lawyers are both superintelligent and the arguments are so long that no human could ever possibility check them all the way through; the lawyers instead read each other’s arguments and then try to point the Jury at the particular places where the holes are in the opponent’s argument without going through the whole thing end-to-end.”

I rest my case.

Anyway, HCH and debate have since been followed by various other successors, which improve on their predecessors mostly by adding more boxes and arrows and loops and sometimes even multiple colors of arrows to the diagram describing the setup. Presumably the strategy is to make it complicated enough that it no longer obviously corresponds to some strategy which already fails in practice, and then we can bury our heads in the sand and pretend that We Just Don’t Know whether it will work and therefore maybe it will work.

(Reminder: in general I don’t reflectively endorse everything in this post; it’s accurately conveying my underlying intuitions, not my all-things-considered judgement. That last bit in particular was probably overly harsh.)

The Ought Experiment

I have a hypothesis about problem factorization research. My guess is that, to kids fresh out of the ivory tower with minimal work experience at actual companies, it seems totally plausible that humans can factorize problems well. After all, we manufacture all sorts of things on production lines, right? Ask someone who’s worked in a non-academia cognitive job for a while (like e.g. a tech company), at a company with more than a dozen people, and they’ll be like “lolwut obviously humans don’t factorize problems well, have you ever seen an actual company?”. I’d love to test this theory, please give feedback in the comments about your own work experience and thoughts on problem factorization.

Anyway, for someone either totally ignorant of the giant onslaught of evidence provided by day-to-day economic reality, or trying to ignore the giant onslaught of evidence in order to avoid their hopes being crushed, it apparently seems like We Just Don’t Know whether humans can factorize cognitive problems well. Sort of like We Just Don’t Know whether a covid test works until after the FDA finishes its trials, even after the test has been approved in the EU ok that’s a little too harsh even for this post.

So Ought went out and tested it experimentally. (Which, sarcasm aside, was a great thing to do.)

The experiment setup: a group of people are given a Project Euler problem. The first person receives the problem, has five minutes to work on it, and records their thoughts in a google doc. The doc is then passed to the next person, who works on it for five minutes recording their thoughts in the doc, and so on down the line. (Note: I’m not sure it was 5 minutes exactly, but something like that.) As long as the humans are able to factor the problem into 5-minute-size chunks without too much overhead, they should be able to efficiently solve it this way.

So what actually happened?

The story I got from a participant is: it sucked. The google doc was mostly useless, you’d spend five minutes just trying to catch up and summarize, people constantly repeated work, and progress was mostly not cumulative. Then, eventually, one person would just ignore the google doc and manage to solve the whole problem in five minutes. (This was, supposedly, usually the same person.) So, in short, the humans utterly failed to factor the problems well, exactly as one would (very strongly) expect from seeing real-world companies in action.

This story basically matches the official write-up of the results.

So Ought said “Oops” and moved on to greener pastures lol no, last I heard Ought is still trying to figure out if better interface design and some ML integration can make problem factorization work. Which, to their credit, would be insanely valuable if they could do it.

That said, I originally heard about HCH and the then-upcoming Ought experiment from Paul Christiano in the summer of 2019. It was immediately very obvious to me that HCH was hopeless (for basically the reasons discussed here); at the time I asked Paul “So when the Ought experiments inevitably fail completely, what’s the fallback plan?”. And he basically said “back to more foundational research”. And to Paul’s credit, three years and an Ought experiment later, he’s now basically moved on to more foundational research.


About a year ago, Cotra proposed a different class of problem factorization experiments: “sandwiching”. We start with some ML model which has lots of knowledge from many different fields, like GPT-n. We also have a human who has a domain-specific problem to solve (like e.g. a coding problem, or a translation to another language) but lacks the relevant domain knowledge (e.g. coding skills, or language fluency). The problem, roughly speaking, is to get the ML model and the human to work as a team, and produce an outcome at-least-as-good as a human expert in the domain. In other words, we want to factorize the “expert knowledge” and the “having a use-case” parts of the problem.

(The actual sandwiching experiment proposal adds some pieces which I claim aren’t particularly relevant to the point here.)

I love this as an experiment idea. It really nicely captures the core kind of factorization needed for factorization-based alignment to work. But Cotra makes one claim I don’t buy: that We Just Don’t Know how such experiments will turn out, or how hard sandwiching will be for cognitive problems in general. I claim that the results are very predictable, because things very much like this already happen all the time in practice.

For instance: consider a lawyer and a business owner putting together a contract. The business owner has a rough intuitive idea of what they want, but lacks expertise on contracts/​law. The lawyer has lots of knowledge about contracts/​law, but doesn’t know what the business owner wants. The business owner is like our non-expert humans; the lawyer is like GPT.

In this analogy, the analogue of an expert human would be a business owner who is also an expert in contracts/​law. The analogue of the “sandwich problem” would be to get the lawyer + non-expert business-owner to come up with a contract as good as the expert business-owner would. This sort of problem has been around for centuries, and I don’t think we have a good solution in practice; I’d expect the expert business-owner to usually come up with a much better contract.

This sort of problem comes up all the time in real-world businesses. We could just as easily consider a product designer at a tech startup (who knows what they want but little about coding), an engineer (who knows lots about coding but doesn’t understand what the designer wants), versus a product designer who’s also a fluent coder and familiar with the code base. I’ve experienced this one first-hand; the expert product designer is way better. Or, consider a well-intentioned mortgage salesman, who wants to get their customer the best mortgage for them, and the customer who understands the specifics of their own life but knows nothing about mortgages. Will they end up with as good a mortgage as a customer who has expertise in mortgages themselves? Probably not. (I’ve seen this one first-hand too.)

There’s tons of real-life sandwiching problems, and tons of economic incentive to solve them, yet we do not have good general-purpose solutions.

The Next Generation

Back in 2019, I heard Paul’s HCH proposal, heard about the Ought experiment, and concluded that this bad idea was already on track to self-correct via experimental feedback. Those are the best kind of bad ideas. I wrote up some of the relevant underlying principles (Coordination as a Scarce Resource and Interfaces as a Scarce Resource), but mostly waited for the problem to solve itself. And I think that mostly worked… for Paul.

But meanwhile, over the past year or so, the field has seen a massive influx of bright-eyed new alignment researchers fresh out of college/​grad school, with minimal work experience in industry. And of course most of them don’t read through most of the enormous, undistilled, and very poorly indexed corpus of failed attempts from the past ten years. (And it probably doesn’t help that a plurality come through the AGI Safety Fundamentals course, which last time I checked had a whole section on problem factorization but, to my knowledge, didn’t even mention the Ought experiment or the massive pile of close real-world economic analogues. It does include two papers which got ok results by picking easy-to-decompose tasks and hard-coding the decompositions.) So we have a perfect recipe for people who will see problem factorization and think “oh, hey, that could maybe work!”.

If we’re lucky, hopefully some of the onslaught of bright-eyed new researchers will attempt their own experiments (like e.g. sandwiching) and manage to self-correct, but at this point new researchers are pouring in faster than any experiments are likely to proceed, so probably the number of people pursuing this particular dead end will go up over time.