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What are the differences between all the iterative/recursive approaches to AI alignment?

by riceissa2 min read21st Sep 201914 comments


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Humans Consulting HCHIterated Amplification Factored CognitionAI
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

I have been trying to understand all the iterative/recursive approaches to AI alignment. The approaches I am aware of are:

  • ALBA (I get the vague impression that this has been superseded by iterated amplification, so I haven't looked into it)
  • HCH (weak and strong)
  • Iterated amplification (IDA)
  • Debate
  • Meta-execution
  • (Recursive) reward modeling
  • Factored cognition
  • Factored evaluation

(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:

  • capability of agents: I think in HCH, the agents are human-level. In the other approaches, my understanding is that the capability of the agents increases as more and more rounds of amplification/distillation take place.
  • allowed communication: It seems like weak and strong HCH differ in the kind of communication that is allowed between the assistants (with strong HCH allowing more flexible communication). Within IDA, there is low bandwidth vs high bandwidth oversight, which seems like a similar parameter. I'm not sure what the other approaches allow.
  • training method during distillation step: I think IDA leaves the training method flexible. According to this post, factored cognition seems to use imitation learning and factored evaluation seems to use reinforcement learning. I think recursive reward modeling also uses reinforcement learning. HCH seems to be just about the amplification step (?), so no training method is used. I'm not sure about the others.
  • entity who "splits the questions", coordinates everything during amplification, or selects the branches: In factored cognition, factored evaluation, IDA, and HCH, it seems like the human splits the questions. In Debate, the branches are chosen by the two AIs in the debate (who are in an adversarial relationship).
  • entity who does the evaluation/gives feedback ("the overseer"): It seems like in factored evaluation, the human gives feedback. In Debate, the final judgment is provided by the human. My understanding is that in IDA, the nature of the overseer is flexible ("For example, Arthur could advise Hugh on how to define a better overseer; Arthur could offer advice in real-time to help Hugh be a better overseer; or Arthur could directly act as an overseer for his more powerful successor").
  • what the overseer does (i.e. what kind of feedback is provided): I think the overseer can be passive/active depending on the distillation method (see my comment here), so maybe this parameter isn't required in a "minimal set".
  • required number of human feedback per round: In Debate, there is one feedback at the end of a debate round. In factored evaluation, it seems like the human must provide feedback at each node in the question tree (or a separate human at each node in the question tree).
  • depth of recursion: It seems like IDA limits the depth of the recursion to one step, whereas the other approaches seem to allow arbitrary depth (see my comment here).
  • separation of task performance vs evaluation/oversight: It seems like in factored evaluation, there is an entity who does the task itself (the experts at the bottom of this diagram), and a separate entity who evaluates the work of the experts (the "factored evaluation" box in the same diagram), but in factored cognition, there is just the entity doing the task.

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!)


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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.