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AI alignment is the work of shaping a model’s behavior so it better follows instructions and broader goals such as truthfulness, fairness, and safety. It is not a switch that guarantees compliance: models learn from imperfect examples, preferences, or rules, and can still misunderstand requests, produce false claims, or behave unsafely.
Why predicting text is not enough
A pretrained language model is trained to predict text from its context. That objective helps it learn patterns in language, but it does not by itself teach the model to carry out a user’s intended task, distinguish a helpful answer from a merely plausible one, or follow safety expectations.
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Alignment training adds signals intended to steer the model toward desired behavior. OpenAI describes reinforcement learning from human feedback (RLHF) as a main technique used in its deployed language-model work, while also acknowledging shortcomings in instruction following, truthfulness, and safety. OpenAI’s overview of its alignment approach is an account of that organization’s work, not a guarantee about every AI system.
How the standard RLHF pipeline works
In the InstructGPT work, RLHF was a sequence of training stages: people first supplied examples, then compared model answers; those comparisons trained a reward model, which in turn guided further model training. The InstructGPT paper documents this process.
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- Collect demonstrations. Labelers write example responses to prompts that illustrate desired behavior.
- Supervised fine-tuning. The pretrained model is trained on those demonstrations so it imitates the examples.
- Collect preferences. Labelers compare multiple candidate responses to a prompt and indicate which they prefer.
- Train a reward model. A separate learned model uses those comparisons to predict which responses people are more likely to prefer.
- Optimize the language model. Reinforcement learning updates the model to earn higher scores from the reward model.
The reward model is a proxy for the preferences represented in its training comparisons, not a direct measurement of truth, safety, or the user’s unstated intent. If those comparisons reward a polished but inaccurate answer, optimization can favor the wrong behavior. The approach can improve responses relative to the signals it receives, but that is not the same as proving that a response is correct or appropriate in every situation.
In OpenAI’s 2022 human evaluations on the authors’ prompt distribution, people preferred outputs from the 1.3-billion-parameter InstructGPT model to those from the 175-billion-parameter GPT-3 model. This is a result for that study’s models, evaluators, and prompt distribution—not evidence that smaller models are generally better. The paper also reports that InstructGPT alignment fine-tuning used less than 2% of GPT-3 pretraining compute and about 20,000 hours of human feedback; those figures describe that project, not typical costs for alignment work generally. The paper and OpenAI’s 2022 overview provide the project context.
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How other approaches shape model behavior
RLHF is not the only way to provide training signals. The approaches below differ in who supplies judgments, whether a learned reward or preference model is involved, and how written rules are used.
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| Approach | Training signal | How it is applied | Evidence and limits |
|---|---|---|---|
| RLHF | Human-written demonstrations and human preference comparisons. | Demonstrations support supervised fine-tuning; comparisons train a reward model used in reinforcement learning. | The InstructGPT paper reports the pipeline and a preference evaluation on its prompt distribution. That result does not establish general truthfulness or safety. InstructGPT paper |
| Constitutional AI / RLAIF | Written principles selected by people, plus AI-generated critiques, revisions, and preferences. | In the supervised phase, a model critiques and revises outputs, then trains on revised responses. In the reinforcement-learning phase, a model judges candidate responses; those preferences train a preference model that supplies a reward signal. Anthropic calls this reinforcement learning from AI feedback, or RLAIF. | Some judgments come from AI rather than human preference labels, but people still choose the constitution. The method description does not establish that its principles resolve every ambiguity or prevent all harmful outputs. Anthropic’s method description |
| Deliberative alignment | Explicit safety specifications. | Training teaches the model to reason over specifications when responding, rather than using a specification only to generate labels. | OpenAI presents this as a published method; the description is not proof that specification reasoning eliminates safety failures. OpenAI’s paper and overview |
| Rule-Based Rewards | Explicit rules used as reward components. | Rules contribute directly to rewards intended to improve safety behavior, rather than relying only on extensive human preference data. | OpenAI’s 2024 article describes its approach; it does not establish that rules alone cover every context or failure mode. OpenAI’s description |
These methods are not interchangeable recipes. Preference training learns from judgments about particular answers; written specifications make intended rules more explicit; AI feedback can apply selected rules at scale. Each still depends on the quality and scope of its signal, and on how the model handles cases the examples or rules do not settle.
Why human intent is difficult to specify
There is no universally agreed list of human preferences that can be copied into a training set. What counts as helpful, fair, or safe can depend on context and culture, and different people may disagree about which value should take priority in a hard case. OpenAI’s discussion of safety and alignment describes intent and values as nuanced and context-dependent. OpenAI’s safety and alignment overview
One organizational response is to seek public input. OpenAI says its 2025 collective-alignment effort gathered views from over 1,000 people worldwide, published an input dataset, and adopted some proposed Model Spec changes. The participant count describes that consultation; it does not show that its participants represent every community affected by AI systems. OpenAI’s collective-alignment update
Even a written specification may contain tensions: two principles can point toward different answers, or a rule may not clearly cover a new situation. Anthropic Alignment Science reports generating over 300,000 scenarios to stress-test competing principles and observing different response patterns among the frontier models it tested. That is the size and scope of a study’s scenario set, not a count of real-world alignment failures. Anthropic’s 2025 stress test
What alignment training can and cannot establish
Training can make a model more likely to produce responses favored by its examples, feedback, or specifications. It cannot by itself establish that the model will be truthful, safe, or reliably compliant in every setting. OpenAI notes failures in all three areas for deployed language models. OpenAI’s account of alignment limitations
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There is also a difference between producing a desirable answer in training and behaving robustly when conditions change. Anthropic’s alignment-faking work examines a constrained experimental setup and presents its findings as a starting point. It is a reason to investigate robustness under training and monitoring, not evidence that deployed AI systems generally fake alignment. Anthropic Alignment Science’s 2025 work
Quick Recap
- Preference is not truth: a preferred answer can still be factually wrong.
- A reward is a proxy: optimizing a learned score can miss the intention behind the examples or comparisons.
- Rules have boundaries: specifications can be incomplete, ambiguous, or in conflict.
- Evaluation has scope: a result applies to the models, prompts, people, and conditions actually tested.
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