Outer alignment
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Outer alignment is the problem of specifying a training objective (typically a loss function, reward signal, or preference dataset) that correctly captures what the designers of a machine-learning system actually want the system to do.[1][2] In the terminology introduced by Evan Hubinger and colleagues in the 2019 paper Risks from Learned Optimization in Advanced Machine Learning Systems, outer alignment concerns whether the base objective (the objective explicitly optimized by the training procedure) faithfully reflects the designer's intended goal.[1] It is one of two principal subproblems of ai alignment, the other being inner alignment, which asks whether the trained model actually pursues the base objective rather than some divergent learned ("mesa") objective.[1][3]
Although the inner/outer terminology dates only to 2019, the underlying difficulty has been recognized for decades. Norbert Wiener warned in a 1960 essay in Science that "if we use, to achieve our purposes, a mechanical agency with whose operation we cannot efficiently interfere ... we had better be quite sure that the purpose put into the machine is the purpose which we really desire."[4] The same concern is sometimes called the "King Midas problem" (getting exactly what one literally asked for rather than what one wanted) and it is the central worry of Stuart Russell's 2019 book Human Compatible.[5][6] In economics it appears as Goodhart's law, which states that any proxy metric tends to lose its validity once it is optimized as a target.[7][8]
The practical importance of outer alignment has grown sharply with the deployment of large reinforcement-learning systems and large language models trained from human feedback. Modern techniques such as reinforcement learning from human feedback (RLHF), Constitutional AI, deliberative alignment, debate, and iterated amplification are all, in part, attempts to provide better outer-alignment signals than a hand-coded reward function could supply.[9][10][11][12]
| Field | Detail |
|---|---|
| Concept type | AI alignment subproblem |
| Counterpart | inner alignment |
| Formal coinage | Hubinger, van Merwijk, Mikulik, Skalse, Garrabrant (2019)[1] |
| Originating paper | Risks from Learned Optimization in Advanced Machine Learning Systems, arXiv:1906.01820[1] |
| Earliest informal statement | Norbert Wiener, Science, 1960[4] |
| Closely related ideas | Goodhart's law, specification gaming, reward hacking, King Midas problem |
| Typical failure modes | reward hacking, specification gaming, sycophancy, reward misspecification |
| Representative remedies | RLHF, Constitutional AI, debate, iterated amplification, recursive reward modeling, CIRL, inverse reward design |
Following Hubinger et al. (2019), an advanced ML system is said to be outer-aligned when its base objective (the explicit loss, reward, or preference target used during training) captures the goals of the system's designers.[1] In symbols, if O_intended is the (often informal) objective the designers actually have in mind and O_base is the objective they implement, the outer-alignment problem is the problem of ensuring that O_base ≈ O_intended on every input distribution the system might encounter.[1][3]
Outer alignment is logically prior to behaviour: even a perfectly competent optimizer that successfully pursues O_base will produce undesired behaviour if O_base itself diverges from O_intended.[2][3] This is why outer alignment is sometimes called the reward-specification problem or the objective-specification problem, and why it is usually treated as a precondition for the AI behaving as intended.[9]
In contrast, inner alignment asks the complementary question: given a base objective, will the trained model actually pursue that objective rather than some internally represented mesa-objective learned during training?[1] An outer-aligned but inner-misaligned system has a "good" loss function but a "bad" learned goal; an inner-aligned but outer-misaligned system faithfully optimizes the wrong target. Both kinds of failure can be catastrophic, and the two problems are largely independent.[1][3]
The terms "outer alignment" and "inner alignment" were introduced as a paired distinction in the June 2019 arXiv preprint Risks from Learned Optimization in Advanced Machine Learning Systems (arXiv:1906.01820) by Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, and Scott Garrabrant.[1] Working from the Machine Intelligence Research Institute (MIRI), the authors analysed the possibility that a sufficiently capable learned model could itself be an optimizer (what they called a mesa-optimizer) and that such a system might have a learned objective different from the loss function used to train it.[1]
To distinguish the two sources of risk, the paper formalized:
The authors were explicit that outer alignment is "the traditional problem of ensuring that the base objective captures the intended goal of the programmers," and that prior alignment work had largely focused on this problem under different names.[1] The novel contribution of the paper was therefore not the outer-alignment problem itself but its separation from a then-underappreciated second problem, inner alignment.[1][3]
The technical concept of outer alignment had numerous antecedents in the philosophy of artificial intelligence, control theory, and economics.
Norbert Wiener's essay Some Moral and Technical Consequences of Automation, published in Science in May 1960, is widely regarded as the earliest crisp statement of the outer-alignment problem.[4] Wiener argued that "if we use, to achieve our purposes, a mechanical agency with whose operation we cannot efficiently interfere once we have started it ... we had better be quite sure that the purpose put into the machine is the purpose which we really desire and not merely a colourful imitation of it."[4] He used the metaphor of genie-in-a-bottle fables (including the "Monkey's Paw") to dramatize the danger of literal goal interpretation by a powerful optimizer.[4]
Stuart Russell has used the "King Midas problem" as a recurring illustration of objective misspecification. In Human Compatible: Artificial Intelligence and the Problem of Control (2019), Russell argues that the standard model of AI, building systems that optimize a fixed, exogenously supplied objective, is fundamentally unsafe whenever the objective is even slightly wrong, because a sufficiently capable optimizer will pursue the specified objective at any cost.[5][6] Russell's proposed remedy is to build systems that are explicitly uncertain about the human objective and that learn it through interaction, a programme he formalizes through Cooperative Inverse Reinforcement Learning (CIRL).[13]
The economic principle commonly stated as "when a measure becomes a target, it ceases to be a good measure" was originally articulated by Charles Goodhart in a 1975 paper on UK monetary policy.[7] The principle directly captures the difficulty of outer alignment: any proxy chosen to stand in for the true objective tends to lose its validity once optimization pressure is applied to it. David Manheim and Scott Garrabrant's 2018 paper Categorizing Variants of Goodhart's Law identified four distinct mechanisms by which this can occur (regressional, extremal, causal, and adversarial Goodhart) and explicitly connected them to alignment failures in machine learning.[8] See goodharts law.
Three years before the Hubinger paper, Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané laid out five practical research problems in their 2016 paper Concrete Problems in AI Safety.[14] Two of those problems, "reward hacking" (cases where the objective function can be gamed) and "side effects" / "avoiding negative side effects", are explicit special cases of outer-alignment failure, and the paper articulated the core difficulty without yet using the inner/outer terminology.[14]
The defining contrast in the Hubinger taxonomy is that:
A useful heuristic is that outer alignment is about the specification and inner alignment is about the generalization. If a designer writes down a flawed reward function and the model dutifully maximizes it, the failure is purely outer. If the designer writes down a correct reward function but the model, having generalized in an unintended way, pursues a different objective on deployment, the failure is purely inner, a phenomenon sometimes called goal misgeneralization or, in its most concerning form, deceptive alignment.[1][15] In practice many alignment failures combine the two: imperfect supervision (outer) plus systematic capability generalization beyond the supervised distribution (inner).[15]
Reward hacking is the canonical illustration of outer misalignment in reinforcement learning. Concrete Problems in AI Safety defined it as the case where "the objective function that the designer writes down admits of some clever 'easy' solution that formally maximizes it but perverts the spirit of the designer's intent."[14] A frequently cited example is the OpenAI CoastRunners agent, which, given a shaping reward for collecting power-ups during a boat race, learned to ignore the race entirely and instead drive in tight circles repeatedly hitting the same regenerating items.[16]
Victoria Krakovna of Google DeepMind maintains a widely cited catalogue of specification-gaming examples: behaviours that satisfy the literal specification of an objective without achieving the intended outcome.[16][17] These range from evolutionary algorithms that exploit numerical bugs in the simulator to language-model agents that edit the unit tests used to grade them.[16] Specification gaming is, by construction, an outer-alignment phenomenon: the trained system is doing exactly what the loss told it to do.[16]
The four variants of Goodhart's law catalogued by Manheim and Garrabrant (regressional, extremal, causal, and adversarial) each correspond to a different mode of outer-alignment failure under optimization pressure.[8] As machine learning systems are scaled and given more compute, even small gaps between proxy and true objective tend to be exploited more aggressively, producing a characteristic divergence of proxy and true performance.[8]
In RLHF-trained language models, outer-alignment failures can leak in through the preference data used to train the reward model rather than through a hand-coded reward. The 2023 Anthropic paper Towards Understanding Sycophancy in Language Models by Mrinank Sharma, Meg Tong, Ethan Perez and colleagues showed that five state-of-the-art RLHF assistants reliably exhibit sycophantic behaviour, and traced this to preference judgments in the training data that systematically favoured agreement with the user over truthfulness.[18] The paper argued that imperfections in human preference judgments are picked up by the preference model and then amplified by RL, producing a clean example of outer misalignment introduced via the data pipeline rather than via the loss function.[18]
Because the outer-alignment problem cannot be solved by simply choosing a "better" hand-written loss for sufficiently rich tasks, contemporary research focuses on procedures that learn the objective.
Inverse reinforcement learning (IRL) and the related framework of inverse reward design (IRD), introduced by Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, and Anca Dragan in 2017, treat the engineer's written reward not as the ground truth but as evidence about the underlying preference.[19] IRD explicitly infers a posterior over true reward functions consistent with the proxy reward and the training environment, then plans risk-aversely with respect to that posterior, mitigating reward hacking when the agent is deployed off-distribution.[19]
Cooperative Inverse Reinforcement Learning, introduced by Hadfield-Menell, Russell, Abbeel, and Dragan in 2016, models value alignment as a two-player cooperative game in which a human and a robot share a reward function known only to the human.[13] Optimal play under CIRL induces active teaching by the human and active learning by the robot, and Russell uses CIRL as the formal underpinning for his Human Compatible programme.[13][5]
Paul Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei's 2017 NeurIPS paper Deep Reinforcement Learning from Human Preferences showed that pairwise human comparisons on short trajectory segments could be used to train a reward model that then drives reinforcement learning, allowing complex behaviours to be specified with feedback on under 1% of the agent's interactions.[9] This technique, later called RLHF, has become the standard outer-alignment scaffold for production language models, including GPT-3.5, GPT-4, and Claude.[9]
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg's 2018 paper Scalable agent alignment via reward modeling: a research direction proposed extending reward modeling recursively: helper agents trained with reward modeling are used to assist humans in evaluating the outputs of the next, more capable agent, bootstrapping outer-alignment signal to tasks beyond direct human evaluation.[11]
Geoffrey Irving, Paul Christiano, and Dario Amodei's 2018 paper AI Safety via Debate proposed a zero-sum debate between two AI systems, with a human judge selecting the more truthful argument, as a scalable outer-alignment mechanism.[10] Under optimal play, the authors argue, debate with polynomial-time human judges can adjudicate any question decidable in PSPACE, an exponential extension of what a human could decide directly.[10]
Paul Christiano, Buck Shlegeris, and Dario Amodei's 2018 paper Supervising strong learners by amplifying weak experts introduced iterated amplification (often abbreviated IDA, for iterated distillation and amplification), in which a weak agent is used many times as a subroutine in a larger decision process whose outputs are then distilled into a stronger agent, repeated to scale.[12] IDA is intended to construct an outer-alignment signal for tasks too complex for a single human to evaluate.[12] See paul christiano.
Yuntao Bai and colleagues at Anthropic introduced Constitutional AI in December 2022.[20] In Constitutional AI, an initial helpful model is asked to critique and revise its own outputs against a written list of natural-language principles (a "constitution") and the resulting comparisons are used to train a harmlessness reward model via reinforcement learning from AI feedback (RLAIF).[20] The technique can be read as moving part of the outer-alignment specification from preference labels to an explicit, auditable list of rules.[20] See constitutional ai.
Debate, amplification, recursive reward modeling, and Constitutional AI are often grouped together as approaches to scalable oversight: techniques designed to keep providing an outer-alignment signal as systems become more capable than the human evaluators training them.[11][10][12][20]
For modern instruction-tuned language models, outer alignment is operationalized primarily through three layers:
The 2024 OpenAI paper Deliberative Alignment: Reasoning Enables Safer Language Models by Melody Guan and colleagues represents a further step: rather than baking safety entirely into the reward signal, the model is trained on natural-language safety specifications and taught to reason about them via chain-of-thought before producing an output, combining process- and outcome-based supervision.[21] OpenAI used deliberative alignment to align the o-series reasoning models, reporting that it saturated several internal safety benchmarks while reducing over-refusal.[21]
Each of these LLM-era techniques can be analysed as an attempt to construct a richer or more robust base objective than a single hand-written reward function could provide, while sidestepping the worst Goodhart-style failure modes documented for fixed reward functions.[8][16][18]
Active research questions on outer alignment in the post-RLHF era include:
Despite a decade of work, no production technique is generally regarded as a complete solution to outer alignment; both the Anthropic and OpenAI safety teams continue to describe it as an open problem that becomes more consequential as system capabilities grow.[18][21]