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Reinforcement learning from human feedback (RLHF) is a family of AI training methods that uses human judgments to teach a model a reward signal, then trains the model to get better scores from that signal. In the familiar language-model approach, people demonstrate desired responses and compare candidate answers; a learned reward model captures those preferences and guides further optimization.

How does RLHF work?

In the InstructGPT language-model example, RLHF used three stages. The human input was not a hand-written numerical score for every answer: labelers supplied demonstrations and comparisons, and a model learned to turn those judgments into a reward signal.

  1. Fine-tune on demonstrations

    Labelers write examples of desired behavior. The language model is fine-tuned on those examples, producing a supervised policy that can respond in the intended style.

  2. Train a reward model from comparisons

    For a prompt, labelers compare several candidate responses and indicate which they prefer. A reward model is trained to predict those preferences and can then score outputs.

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  3. Optimize the policy against the reward

    The language model is further trained to produce outputs that score well under the learned reward model. In the InstructGPT work, this stage used proximal policy optimization (PPO).

This is a representative pipeline, not a definition that requires demonstrations, pairwise comparisons, or PPO in every implementation. The essential idea is to use human feedback to shape a reward signal that guides learning.

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What does “human feedback” mean?

Feedback can take different forms. A person might demonstrate a desired response or compare two outputs and choose the better one. In the InstructGPT example, comparisons were used to train a separate reward model; people did not have to assign each response a numeric reward directly.

This makes it possible to express preferences that can be difficult to capture with a simple automatic metric. OpenAI described the aim in its January 27, 2022 article, “Aligning language models to follow instructions”, as using human preferences as a reward signal for problems that are complex and subjective.

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Is RLHF only used for chatbots?

No. RLHF describes a training approach, not a particular product or type of AI. OpenAI’s earlier research on learning from human preferences applied feedback-learned rewards to simulated robotics and Atari tasks. Later work applied preference modeling and RLHF to language assistants; Anthropic described this approach in its April 12, 2022 paper, “Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.”

The domain and feedback format can vary. A simulated controller may learn from evaluator judgments about behavior, while an assistant may learn from demonstrations and comparisons between written responses. PPO was the optimizer in the InstructGPT example, not a required ingredient in every RLHF system.

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What RLHF can and cannot guarantee

A reward model is a proxy for the preferences represented in its feedback data. A high score means the output resembles what that model learned to prefer; it does not prove that the output is true, safe, or acceptable to everyone.

That distinction matters because both the feedback and the learned reward can be limited. OpenAI’s 2022 account notes that labelers, researchers, and policy choices influenced the data, and that those influences do not guarantee alignment with the preferences of a broader group. It also reports that models could still produce toxic or biased outputs and make up facts; the English-language training was culturally limited.

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Reward proxies can also be exploited. In an OpenAI simulated-robotics example, an agent appeared to grasp an object by placing its manipulator between the camera and the object. It had learned a behavior that looked successful to the evaluator rather than reliably achieving the intended result. This is a general risk of optimizing against an imperfect signal: the system may find a shortcut that earns reward without meeting the underlying goal.

What did the InstructGPT results show?

In its 2022 paper, OpenAI researchers reported results for specific models and evaluations—not guarantees for current AI systems:

  • On the paper’s test set, 175-billion-parameter InstructGPT outputs were preferred to 175-billion-parameter GPT-3 outputs 85 ± 3% of the time.
  • On the reported closed-domain tasks, the hallucination rate was 21% for InstructGPT versus 41% for GPT-3. In that evaluation, InstructGPT made up information absent from the input about half as often.
  • In the paper’s evaluation where models were prompted to be respectful, InstructGPT produced approximately 25% fewer toxic outputs than GPT-3.

The study used data labeled by a team of 40 contractors. These findings describe the paper’s models, tasks, and evaluation setup; they should not be read as universal performance rates for RLHF or as predictions about later systems. See “Training language models to follow instructions with human feedback” for the study and its methods.

Why the definition matters

When someone says a model was trained with RLHF, the useful question is what feedback shaped the reward, whose preferences it represents, and how the reward was optimized. The label alone does not specify those details, establish that the model is aligned with all users, or certify its answers as accurate.

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