Richard Ngo

Former AI safety research engineer, now AI governance researcher at OpenAI. Blog: thinkingcomplete.com

Sequences

Shaping safer goals
AGI safety from first principles

Wiki Contributions

Comments

Supervised data seems way more fine-grained in what you are getting the AI to do. It's just that supervised fine-tuning is worse.

My (pretty uninformed) guess here is that supervised fine-tuning vs RLHF has relatively modest differences in terms of producing good responses, but bigger differences in terms of avoiding bad responses. And it seems reasonable to model decisions about product deployments as being driven in large part by how well you can get AI not to do what you don't want it to do.

Putting my money where my mouth is: I just uploaded a (significantly revised) version of my Alignment Problem position paper, where I attempt to describe the AGI alignment problem as rigorously as possible. The current version only has "policy learns to care about reward directly" as a footnote; I can imagine updating it based on the outcome of this discussion though.

Note that the "without countermeasures" post consistently discusses both possibilities

Yepp, agreed, the thing I'm objecting to is how you mainly focus on the reward case, and then say "but the same dynamics apply in other cases too..."

I do place a ton of emphasis on the fact that Alex enacts a policy which has the empirical effect of maximizing reward, but that's distinct from being confident in the motivations that give rise to that policy.

The problem is that you need to reason about generalization to novel situations somehow, and in practice that ends up being by reasoning about the underlying motivations (whether implicitly or explicitly).

I strongly disagree with the "best case" thing. Like, policies could just learn human values! It's not that implausible.

If I had to try point to the crux here, it might be "how much selection pressure is needed to make policies learn goals that are abstractly related to their training data, as opposed to goals that are fairly concretely related to their training data?" Where we both agree that there's some selection pressure towards reward-like goals, and it seems like you expect this to be enough to lead policies to behavior that violates all their existing heuristics, whereas I'm more focused on the regime where there are lots of low-hanging fruit in terms of changes that would make a policy more successful, and so the question of how easy that goal is to learn from its training data is pretty important. (As usual, there's the human analogy: our goals are very strongly biased towards things we have direct observational access to!)

Even setting aside this disagreement, though, I don't like the argumentative structure because the generalization of "reward" to large scales is much less intuitive than the generalization of other concepts (like "make money") to large scales - in part because directly having a goal of reward is a kinda counterintuitive self-referential thing.

(Written quickly and not very carefully.)

I think it's worth stating publicly that I have a significant disagreement with a number of recent presentations of AI risk, in particular Ajeya's "Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover", and Cohen et al.'s "Advanced artificial agents intervene in the provision of reward". They focus on policies learning the goal of getting high reward. But I have two problems with this:

  1. I expect "reward" to be a hard goal to learn, because it's a pretty abstract concept and not closely related to the direct observations that policies are going to receive. If you keep training policies, maybe they'd converge to it eventually, but my guess is that this would take long enough that we'd already have superhuman AIs which would either have killed us or solved alignment for us (or at least started using gradient hacking strategies which undermine the "convergence" argument). Analogously, humans don't care very much at all about the specific connections between our reward centers and the rest of our brains - insofar as we do want to influence them it's because we care about much more directly-observable phenomena like pain and pleasure.
  2. Even once you learn a goal like that, it's far from clear that it'd generalize in ways which lead to power-seeking. "Reward" is not a very natural concept, it doesn't apply outside training, and even within training it's dependent on the specific training algorithm you use. Trying to imagine what a generalized goal of "reward" would cash out to gets pretty weird. As one example: it means that every time you deploy the policy without the intention of rewarding it, then its key priority would be convincing you to inserting that trajectory into the training data. (It might be instructive to think about what the rewards would need to be for that not to happen. Below 0? But the 0 point is arbitrary...) That seems pretty noticeable! But wouldn't it be deceptive? Well, only within the scope of its current episode, because trying to get higher reward in other episodes is never positively reinforced. Wouldn't it learn the high-level concept of "reward" in general, in a way that's abstracted from any specific episode? That feels analogous to a human learning to care about "genetic fitness" but not distinguishing between their own genetic fitness and the genetic fitness of other species. And remember point 1: the question is not whether the policy learns it eventually, but rather whether it learns it before it learns all the other things that make our current approaches to alignment obsolete.

At a high level, this comment is related to Alex Turner's Reward is not the optimization target. I think he's making an important underlying point there, but I'm also not going as far as he is. He says "I don't see a strong reason to focus on the “reward optimizer” hypothesis." I think there's a pretty good reason to focus on it - namely that we're reinforcing policies for getting high reward. I just think that other people have focused on it too much, and not carefully enough - e.g. the "without specific countermeasures" claim that Ajeya makes seems too strong, if the effects she's talking about might only arise significantly above human level. Overall I'm concerned that reasoning about "the goal of getting high reward" is too anthropomorphic and is a bad way to present the argument to ML researchers in particular.

In general I think it's better to reason in terms of continuous variables like "how helpful is the iterative design loop" rather than "does it work or does it fail"?

My argument is more naturally phrased in the continuous setting, but if I translated it into the binary setting: the problem with your argument is that conditional on the first being wrong, then the second is not very action-guiding. E.g. conditional on the first, then the most impactful thing is probably to aim towards worlds in which we do hit or miss by a little bit; and that might still be true if it's 5% of worlds rather than 50% of worlds.

Upon further thought, I have another hypothesis about why there seems like a gap here. You claim here that the distribution is bimodal, but your previous claim ("I do in fact think that relying on an iterative design loop fails for aligning AGI, with probability close to 1") suggests you don't actually think there's significant probability on the lower mode, you essentially think it's unimodal on the "iterative design fails" worlds.

I personally disagree with both the "significant probability on both modes, but not in between" hypothesis, and the "unimodal on iterative design fails" hypothesis, but I think that it's important to be clear about which you're defending - e.g. because if you were defending the former, then I'd want to dig into what you thought the first mode would actually look like and whether we could extend it to harder cases, whereas I wouldn't if you were defending the latter.

I think you're just doing the bimodal thing again. Sure, if you condition on worlds in which alignment happens automagically, then it's not valuable to advance the techniques involved. But there's a spectrum of possible difficulty, and in the middle parts there are worlds where RLHF works, but only because we've done a lot of research into it in advance (e.g. exploring things like debate); or where RLHF doesn't work, but finding specific failure cases earlier allowed us to develop better techniques.

in worlds where iterative design works, we probably survive AGI without anybody (intentionally) thinking about RLHF

In worlds where iterative design works, it works by iteratively designing some techniques. Why wouldn't RLHF be one of them?

In particular, the excerpts/claims from Get What You Measure are pretty cruxy.

It seems pretty odd to explain this by quoting someone who thinks that this effect is dramatically less important than you do (i.e. nowhere near causing a ~100% probability of iterative design failing). Not gonna debate this on the object level, just flagging that this is very far from the type of thinking that can justifiably get you anywhere near those levels of confidence.

In worlds where the iterative design loop works for alignment, we probably survive AGI. So, if we want to improve humanity’s chances of survival, we should mostly focus on worlds where, for one reason or another, the iterative design loop fails. ... Among the most basic robust design loop failures is problem-hiding. It happens all the time in the real world, and in practice we tend to not find out about the hidden problems until after a disaster occurs. This is why RLHF is such a uniquely terrible strategy: unlike most other alignment schemes, it makes problems less visible rather than more visible. If we can’t see the problem, we can’t iterate on it.

This argument is structurally invalid, because it sets up a false dichotomy between "iterative design loop works" and "iterative design loop fails". Techniques like RLHF do some work towards fixing the problem and some work towards hiding the problem, but your bimodal assumption says that the former can't move us from failure to success. If you've basically ruled out a priori the possibility that RLHF helps at all, then of course it looks like a terrible strategy!

By contrast, suppose that there's a continuous spectrum of possibilities for how well iterative design works, and there's some threshold above which we survive and below which we don't. You can model the development of RLHF techniques as pushing us up the spectrum, but then eventually becoming useless if the threshold is just too high. From this perspective, there's an open question about whether the threshold is within the regime in which RLHF is helpful; I tend to think it will be if not overused.

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