You seem to be thinking of all of these as things-that-can-be-implemented, which I don't think is exactly right. I think you could only call some of these "approaches to alignment", if you mean something that could some day be implemented and lead to aligned AI.
I'll consider two different properties:
Implementation: Theoretical (can't be implemented, takes infinite compute) or Implementable without ML (i.e. with humans, as a result it is very inefficient) or Implementation needs ML (there will still be humans, but the hope is that it will be efficient and competitive with unaligned AI systems).
How learning happens: Task-based (the agent learns to perform a particular task or reason in a particular way) or Reward-based (the agent learns to provide a good reward signal that provides good incentives for some other system). While task-based systems are more elegant and clean when everything works right, we'd expect that in the presence of real world messiness such as optimization difficulty that reward-based systems will be more robust (see Against Mimicry).
I would only consider the Implementation needs ML things to be "approaches to alignment". Anyway, here they are (ignoring ALBA for the same reason you do):
Weak HCH: A theoretical ideal where each human can delegate to other agents. We hope that the result is both superintelligent and aligned. Properties: Theoretical / Task-based
Strong HCH: Like weak HCH, but allowing each human to have a dialog with subagents, and allowing message passing to include pointers to other agents. Properties: Theoretical / Task-based
Meta-execution: A particular implementation method that can deal with the fact that some questions may be too "big" for any one agent to even read the full question. Properties: Not really a recursive approach, it's more a component of other approaches.
Factored cognition: The hypothesis that strong HCH can solve arbitrary tasks. In terms of actual implementation, it's compute-limited strong HCH / meta-execution. Properties: Implementable without ML / Task-based
Factored evaluation: The hypothesis that strong HCH can provide a reward signal for arbitrary tasks. (Less confident of this one, as it hasn't been explained in detail before.) In terms of actual implementation, it's compute-limited strong HCH / meta-execution, where the goal is to provide a reward signal for some task. Properties: Implementable without ML / Reward-based
Iterated amplification (IDA): Approximating strong/weak HCH by training an agent that behaves like depth-limited strong/weak HCH and increasing the effective depth over time. Properties: Implementation needs ML / Task-based
Recursive reward modeling: Approximating strong/weak HCH by training an agent that behaves like depth-limited strong/weak HCH on tasks that are useful for evaluating the task of interest, and on evaluating tasks that are useful for evaluating the task of interest, etc. Properties: Implementation needs ML / Reward-based
Debate: Not really a recursive method at all, but it still depends on the general premise of decomposing thought processes into trees of smaller thoughts (though in this case, the "smaller thoughts" have to be arguments and counterarguments, rather than general considerations). If the Factored cognition hypothesis is false, then debate is unlikely to work.
Thanks! I found this answer really useful.
I have some follow-up questions that I'm hoping you can answer:
I found this immensely helpful overall, thank you!
However, I'm still somewhat confused by meta-execution. Is it essentially a more sophisticated capability amplification strategy that replaces the role filled by "deliberation" in Christiano's IA paper?
It seems like IDA limits the depth of the recursion to one step, whereas the other approaches seem to allow arbitrary depth
At any given point of training, IDA is training the agent to perform one more step of recursion than it is already performing. Repeated application of this lets it reach arbitrary depths eventually.
Is this necessarily true? It seems like this describes what Christiano calls "delegation" in his paper, but wouldn't apply to IDA schemes with other capability amplification methods (such as the other examples in the appendix of "Capability Amplification").
"Depth" only applies to the canonical tree-based implementation of IDA. If you slot in other amplification or distillation procedures, then you won't necessarily have "depths" any more. You'll still have recursion, and that recursion will lead to more and more capability. Where it ends up depends on your initial agent and how good the amplification and distillation procedures are.
Huh, I thought that all amplification/distillation procedures were intended as a way to approximate HCH, which is itself a tree. Can you not meaningfully discuss "this amplification procedure is like an n-depth approximation of HCH at step x", for any amplification procedure?
For example, the internal structure of the distilled agent described in Christiano's paper is unlikely to look anything like a tree. However, my (potentially incorrect?) impression is that the agent's capabilities at step x are identical to an HCH tree of depth x if the underlying learning system is arbitrarily capable.
It's possible that I'm not understanding the difference between "depth", "tree-based" and "recursion" in this context
Can you not meaningfully discuss "this amplification procedure is like an n-depth approximation of HCH at step x", for any amplification procedure?
No, you can't. E.g. If your amplification procedure only allows you to ask a single subagent a single question, that will approximate a linear HCH instead of a tree-based HCH. If your amplification procedure doesn't invoke subagents at all, but instead provides more and more facts to the agent, it doesn't look anything like HCH. The canonical implementations of iterated amplification are trying to approximate HCH though.
For example, the internal structure of the distilled agent described in Christiano's paper is unlikely to look anything like a tree. However, my (potentially incorrect?) impression is that the agent's capabilities at step x are identical to an HCH tree of depth x if the underlying learning system is arbitrarily capable.
That sounds right to me.
I have been trying to understand all the iterative/recursive approaches to AI alignment. The approaches I am aware of are:
(I think that some of these, like HCH, aren't strictly speaking an approach to AI alignment, but they are still iterative/recursive things discussed in the context of AI alignment, so I want to better understand them.)
One way of phrasing what I am trying to do is to come up with a "minimal set" of parameters/dimensions along which to compare these different approaches, so that I can take a basic template, then set the parameters to obtain each of the above approaches as an instance.
Here are the parameters/dimensions that I have come up with so far:
I would appreciate hearing about more parameters/dimensions that I have missed, and also any help filling in some of the values for the parameters (including corrections to any of my speculations above).
Ideally, there would be a table with the parameters as columns and each of the approaches as rows (possibly transposed), like the table in this post. I would be willing to produce such a write-up assuming I am able to fill in enough of the values that it becomes useful.
If anyone thinks this kind of comparison is useless/framed in the wrong way, please let me know. (I would also want to know what the correct framing is!)
