# “Clean” vs. “messy” goal-directedness (Section 2.2.3 of “Scheming AIs”)

This is Section 2.2.3 of my report “[Scheming AIs: Will AIs fake alignment during training in order to get power?](https://arxiv.org/pdf/2311.08379.pdf)”. There’s also a summary of the full report [here](https://www.lesswrong.com/posts/yFofRxg7RRQYCcwFA/new-report-scheming-ais-will-ais-fake-alignment-during) (audio [here](https://joecarlsmithaudio.buzzsprout.com/2034731/13969977-introduction-and-summary-of-scheming-ais-will-ais-fake-alignment-during-training-in-order-to-get-power)). The summary covers most of the main points and technical terms, and I’m hoping that it will provide much of the context necessary to understand individual sections of the report on their own.

Audio version of this section [here](https://www.buzzsprout.com/2034731/13984850), or search "Joe Carlsmith Audio" on your podcast app.

# "Clean" vs. "messy" goal-directedness

We've now discussed two routes to the sort of beyond-episode goals that
might motivate scheming. I want to pause here to note two different ways
of thinking about the type of goal-directedness at stake – what I'll
call "clean goal-directedness" and "messy goal-directedness." We ran
into these differences in the last section, and they'll be relevant in
what follows as well.

I said in section 0.1 that I was going to assume that all the
models we're talking about are goal-directed in some sense. Indeed, I
think most discourse about AI alignment rests on this assumption in one
way or another. In particular: this discourse assumes that the behavior
of certain kinds of advanced AIs will be well-predicted by treating them
as though they are pursuing goals, and doing instrumental reasoning in
pursuit of those goals, in a manner roughly analogous to the sorts of
agents one encounters in economics, game-theory, and human social
life – that is, agents where it makes sense to say things like "this
agent wants X to happen, it knows that if it does Y then X will happen,
so we should expect it do Y."

But especially in the age of neural networks, the AI alignment discourse
has also had to admit a certain kind of agnosticism about the cognitive
mechanisms that will make this sort of talk appropriate. In particular:
at a *conceptual* level, this sort of talk calls to mind a certain kind
of clean distinction between the AI's goals, on the one hand, and its
instrumental reasoning (and its capabilities/"optimization power" more
generally), on the other. That is, roughly, we decompose the AI's
cognition into a "goal slot" and what we might call a "goal-pursuing
engine" – e.g., a world model, a capacity for instrumental reasoning,
other sorts of capabilities, etc. And in talking about models with
different sorts of goals – e.g., schemers, training saints,
mis-generalized non-training-gamers, etc – we generally assume that the
"goal-pursuing engine" is held roughly constant. That is, we're mostly
debating what the AI's "optimization power" will be applied to, not the
*sort* of optimization power at stake. And when one imagines SGD
*changing* an AI's goals, in this context, one mostly imagines it
altering the content of the goal slot, thereby smoothly redirecting the
"goal-pursuing engine" towards a different objective, without needing to
make any changes to the engine itself.

But it's a very open question how much this sort of distinction between
an AI's goals and its goal-pursuing-engine will actually be reflected in
the mechanistic structure of the AI's cognition – the structure that
SGD, in modifying the model, has to intervene on. One can *imagine*
models whose cognition is in some sense cleanly factorable into a goal,
on the one hand, and a goal-pursuing-engine, on the other (I'll call
this "clean" goal-directedness). But one can also imagine models whose
goal-directedness is much messier – for example, models whose
goal-directedness emerges from a tangled kludge of locally-activated
heuristics, impulses, desires, and so on, in a manner that makes it much
harder to draw lines between e.g. terminal goals, instrumental
sub-goals, capabilities, and beliefs (I'll call this "messy"
goal-directedness).

To be clear: I don't, myself, feel fully clear on the distinction here,
and there is a risk of mixing up levels of abstraction (for example, in
some sense, all computation – even the most cleanly goal-directed
kind – is made up of smaller and more local computations that won't,
themselves, seem goal-directed). As another intuition pump, though:
discussions of goal-directedness sometimes draw a distinction between
so-called "[sphex-ish](https://en.wiktionary.org/wiki/sphexish)" systems
(that is, systems whose apparent goal-directedness is in fact the
product of very brittle heuristics that stop promoting the imagined
"goal" if you alter the input distribution a bit), and highly
*non*-sphex-ish systems (that is, systems whose apparent goal-pursuit is
much less brittle, and which will adjust to new circumstances in a
manner that continues to promote the goal in question). Again: very far
from a perspicuous distinction. Insofar as we use it, though, it's
pretty clearly a spectrum rather than a binary. And humans, I suspect,
are somewhere in the middle.

That is: on the one hand, humans pretty clearly have extremely flexible
and adaptable goal-pursuing ability. You can describe an arbitrary task
to a human, and the human will be able to reason instrumentally about
how to accomplish that task, even if they have never performed it
before – and often, to do a decent job on the first try. In that sense,
they have some kind of "repurposable instrumental reasoning
engine" – and we should expect AIs that can perform at human-levels or
better on diverse tasks to have one, too.[^141] Indeed, generality of
this kind is one of the strongest arguments for expecting non-sphex-ish
AI systems. We want our AIs to be able to do *tons* of stuff, and to
adapt successfully to new obstacles and issues as they arise. Explicit
instrumental reasoning is well-suited to this; whereas brittle local
heuristics are not.

On the other hand: a lot of human cognition and behavior seems centrally
driven, not by explicit instrumental reasoning, but by more
locally-activated heuristics, policies, impulses, and desires.[^142]
Thus, for example, maybe you don't want the cookies until you walk by
the jar, and then you find yourself grabbing without having decided to
do so; maybe as a financial trader, or a therapist, or even a CEO, you
lean heavily on gut-instinct and learned tastes/aesthetics/intuitions;
maybe you operate with a heuristic like "honesty is the best policy,"
without explicitly calculating when honesty is or isn't in service of
your final goals. That is, much of human life seems like it's lived at
some hazy and shifting borderline between "auto-pilot" and "explicitly
optimizing for a particular goal" – and it seems possible to move
further in one direction vs. another.[^143] And this is one of the many
reasons it's not always clear how to decompose human cognition into e.g.
terminal goals, instrumental sub-goals, capabilities, and beliefs.

What's more, while pressures to adapt flexibly across a wide variety of
environments generally favor more explicit instrumental reasoning,
pressures to perform quickly and efficiently in a particular *range* of
environments plausibly favor implementing more local heuristics.[^144]
Thus, a trader who has internalized the right rules-of-thumb/tastes/etc
for the bond market will often perform better than one who needs to
reason explicitly about every trade – even though those
rules-of-thumb/tastes/etc would misfire in some other environment, like
trading crypto. So the task-performance of minds with bounded resources,
exposed to a limited diversity of environments – that is, all minds
relevant to our analysis here, even very advanced AIs – won't always
benefit from moving further in the direction of "non-sphex-ish."

Plausibly, then, human-level-ish AIs, and even somewhat-super-human AIs,
will continue to be "sphex-ish" to at least some extent – and
sphex-ishness seems, to me, closely akin to "messy goal-directedness" in
the sense I noted above (i.e., messy goal-directedness is built out of
more sphex-ish components, and seems correspondingly less robust).
Importantly, this sort of sphexish-ness/messy-ness is quite compatible
with worries about alignment, power-seeking, etc – witness, for example,
humans. But I think it's still worth bearing in mind.

In particular, though, I think it may be relevant to the way we approach
different stories about scheming. We ran into one point of relevance in
the last section: namely, that to the extent a model's goals and the
rest of its cognition (e.g., its beliefs, capabilities,
instrumental-reasoning, etc) are not cleanly separable, we plausibly
shouldn't imagine SGD being able to modify a model's goals in particular
(and especially, to modify them via a tiny adjustment to the model's
parameters), and then to immediately see the benefits of the model's
goal-achieving-engine being smoothly repurposed towards those goals.
Rather, turning a non-schemer into a schemer might require more
substantive and holistic modification of the model's heuristics, tastes,
patterns of attention, and so forth.

Relatedly: I think that "messy goal-directedness" complicates an
assumption often employed in comparisons between schemers and other
types of models: namely, the assumption that schemers will be able to
perform approximately just as well as other sorts of models on all the
tasks at stake in training (modulo, perhaps, a little bit extra
cognition devoted to deciding-to-scheme – more below), even though
they're doing so for instrumental reasons rather than out of any
intrinsic interest in the task in question. This makes sense if you
assume that all these models are aiming the same sort of "goal achieving
engine" at a max-reward goal, for one reason or another. But what if
that's not the right description?

Thus, as an extreme human example, consider two applicants for a job on
a factory farm (each with similar intelligence, stamina, physical
fitness, and other generic "capabilities"): Bob, a very compassionate
and soft-hearted animal rights advocate, who is applying for the job in
the hopes of making money to donate to animal-welfare causes; and Fred,
who is totally indifferent to animal welfare (and to the money that the
job offers), but who intrinsically loves performing all of the tasks
that factory farming involves as efficiently as possible.[^145] Who is
likely to perform the job more effectively? In humans, at least:
probably Fred. Bob, after all, would likely push the animals into cages
less violently, he would be distracted by feelings of horror and pain
and disgust, etc; whereas Fred would perform the job with efficiency and
gusto. In this case, Bob is worse at the task, despite his best efforts
to perform it well, because his capabilities and his values are in some
sense mixed together – his compassion, for example, is tied to both.

Of course, AIs could be different in tons of respects – and many humans
are able to perform well in lots of contexts for instrumental reasons
(witness, for example, spies working in governments they actively
despise; or effective altruists working in hedge funds in order to
donate the money to charity; or people, in general, who are just doing
the job for the money). My point here is just that once you start
allowing values, beliefs, heuristics, capabilities, patterns of
attention, and so on to mix together, whether a schemer will be able to
perform at the same level as e.g. a training-saint becomes a more open
question.

## Does scheming require a higher standard of goal-directedness?

As a final point of relevance of "messy goal-directedness" to scheming,
I'll note that there's at least some case to be made that schemers
require a "cleaner" and less "sphex-ish" form of goal-directedness than
some of the other models I've discussed – e.g. training saints. Thus,
for example, consider a model trained via RLHF to respond to various
short-term user requests in a broadly helpful, honest, and harmless way.
Many of these requests will require some sorts of flexible instrumental
reasoning, yes – but it seems plausible to me that a model will also be
getting a lot of its reward by learning the right sorts of local
heuristics, tastes, patterns of attention, etc. That is, that a lot of
its task-performance will be more "sphex-ish" than "explicit
instrumental-reasoning-y." For this model to be a schemer, though (or at
least, a pure schemer who values something quite different from the
specified goal), we need to posit an additional role for explicit
instrumental reasoning – namely, the model's current good behavior needs
to be conditional on its calculation that such behavior conduces to its
power (or the power of some other AIs); and when the time comes, the
model needs to be ready to abandon such behavior and pursue its true
goals. That is, whatever heuristics, local tastes, patterns of attention
etc that give rise to the model's good behavior can't be fully
hard-coded[^146] – they need to be at least partly subsumed by, and
sensitive to, some other kind of instrumental reasoning. Whereas
perhaps, for other models, this is less true.

That said, I've been assuming, and will continue to assume, that all the
models we're considering are at least non-sphex-ish *enough* for the
traditional assumptions of the alignment discourse to apply – in
particular, that they will generalize off distribution in competent ways
predicted by the goals we're attributing to them (e.g., HHH personal
assistants will continue to try to be HHH, gold-coin-seekers will "go
for the gold coins," reward-seekers will "go for reward," etc), and that
they'll engage in the sort of instrumental reasoning required to get
arguments about instrumental convergence off the ground. So in a sense,
we're assuming a reasonably high standard of non-sphex-ishness from the
get-go. I have some intuition that the standard at stake for schemers is
still somewhat higher (perhaps because schemers seem like such paradigm
consequentialists, whereas e.g. training saints seem like they might be
able to be more deontological, virtue-ethical, etc?), but I won't press
the point further here.

Of course, to the extent we don't assume that training is producing a
very goal-directed model *at all*, hypothesizing that training has
created a schemer may well involve hypothesizing a greater degree of
goal-directedness than we would've needed to otherwise. That is,
scheming will often require a higher standard of non-sphex-ishness than
*the training tasks themselves require*. Thus, as an extreme example,
consider
[AlphaStar](https://www.deepmind.com/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii),
a model trained to play Starcraft. AlphaStar is plausibly goal-directed
to some extent – its policy adapts flexibly to certain kinds of
environmental diversity, in a manner that reliably conduces to
winning-at-starcraft – but it's still quite sphex-ish and brittle in
other ways. And to be clear: no one is saying that AlphaStar is a
schemer. But in order to be a schemer (i.e., for AlphaStar's good
performance in training to be explained by its executing a long-term
instrumental strategy for power-seeking), and even modulo the need for
situational awareness, AlphaStar would also need to be substantially
more "goal-directed" than it currently is. That is, in this case,
"somehow be such that you do this goal-directed-ish task" and "do this
goal-directed-ish task because you've calculated that it conduces to
your long-term power after training is complete" plausibly implicate
different standards of goal-directedness. Perhaps, then, the same
dynamic will apply to other, more flexible and advanced forms of
task-performance (e.g., various forms of personal assistance, science,
etc). Yes, those forms will require more in the way of general-purpose
goal-directedness than AlphaStar displays. But perhaps they will require
*less* than scheming requires, such that hypothesizing that the relevant
model is a schemer will require hypothesizing a more substantive degree
of goal-directedness than we would've needed to otherwise.

Indeed, my general sense is that one source of epistemic resistance to
the hypothesis that SGD will select for schemers is the sense in which
hypothesizing a schemer requires leaning on an attribution of
goal-directedness in a way that greater agnosticism about *why* a model
gets high reward need not. That is, prior to hypothesizing schemers,
it's possible to shrug at a model's high-reward behavior and say
something like:

> "This model is a tangle of cognition such that it reliably gets high
> reward on the training distribution. Sure, you can say that it's
> 'goal-directed' if you'd like. I sometimes talk that way too. But all
> I mean is: it reliably gets high reward on the training distribution.
> Yes, in principle, it will also do things off of the training
> distribution. Maybe even: competent-seeming things. But I am not
> making predictions about what those competent-seeming things are, or
> saying that they will be pointed in similar-enough directions, across
> out-of-distribution-inputs, that it makes sense to ascribe to this
> model a coherent 'goal' or set of goals. It's a policy. It gets high
> reward on the training distribution. That's my line, and I'm sticking
> to it."

And against this sort of agnostic, atheoretical backdrop, positing that
the model is probably getting reward *specifically as part of a
long-term strategy to avoid its goals being modified and then get power
later* can seem like a very extreme move in the direction of
conjunctiveness and theory-heavy-ness. That is, we're not just
attributing a goal to the model in some sort of hazy,
who-knows-what-I-mean, does-it-even-matter sense. Rather, we're
specifically going "inside the model's head" and attributing to it
explicit long-term instrumental calculations driven by sophisticated
representations of how to get what it wants.[^147]

However, I think the alignment discourse *in general* is doing this. In
particular: I think the discourse about convergent instrumental
sub-goals requires attributing goals to models in a sense that licenses
talk about strategic instrumental reasoning of this kind. And to be
clear: I'm not saying these attributions are appropriate. In fact,
confusions about goal-directedness (and in particular: over-anchoring on
psychologies that look like (a) expected utility maximizers and (b)
total utilitarians) are one of my top candidates for the ways in which
the discourse about alignment, as a whole, might be substantially
misguided, especially with respect to advanced-but-still-opaque neural
networks whose cognition we don't understand. That is, faced with a
model that seems quite goal-directed on the training-distribution, and
which is getting high reward, one shouldn't just ask where in some
taxonomy of goal-directed models it falls – e.g., whether it's a
training-saint, a mis-generalized non-training-gamer, a
reward-on-the-episode-seeker, some mix of these, etc. One should *also*
ask whether, in fact, such a taxonomy makes overly narrow assumptions
about how to predict this model's behavior in general (for example:
assuming that its out-of-distribution behavior will point in a coherent
direction, that it will engage in instrumental reasoning in pursuit of
the goals in question, etc), such that *none* of the model classes in
the taxonomy are (even roughly) a good fit.

But as I noted in section 0.1, I here want to separate out the question of
whether it makes sense to expect goal-directedness of this kind from the
question of what *sorts* of goal-directed models are more or less
plausible, conditional on getting the sort of goal-directedness that the
alignment discourse tends to assume. Admittedly, to the extent the
different model classes I'm considering require different *sorts* of
goal-directedness, the line between these questions may blur a bit. But
we should be clear about which question we're asking, and not confuse
skepticism about goal-directedness in general for skepticism about
schemers in particular.

[^141]: Thanks to Evan Hubinger for discussion, here.

[^142]: This is a point emphasized, for example, by proponents of "shard
    theory" – see e.g.
    [this](https://www.lesswrong.com/posts/8ccTZ9ZxpJrvnxt4F/shard-theory-in-nine-theses-a-distillation-and-critical#1_Agents_are__well_modeled_as_being__made_up_of_shards)
    summary.

[^143]: Though note that "autopilot" can still encode a non-sphex-ish
    policy.

[^144]: This is a point made in an entry to the [Open Philanthropy
    worldviews
    contest](https://www.openphilanthropy.org/open-philanthropy-ai-worldviews-contest/)
    which, to my knowledge, remains unpublished.

[^145]: I'm adapting this example from one suggested to me in
    conversation with Paul Christiano.

[^146]: Though one can imagine cases where, after a takeover, a schemer
    continues executing these heuristics to some extent, at least
    initially, because it hasn't yet been able to fully "shake off" all
    that training. And relatedly, cases where these heuristics etc play
    some ongoing role in shaping the schemer's values.

[^147]: *Plus* we're positing additional claims about training-gaming
    being a good instrumental strategy because it prevents
    goal-modification and leads to future escape/take-over
    opportunities, which feels additionally conjunctive.