A field guide to AI positions

Reinforcement learning from human feedback (RLHF)

Inside a model.

Source is over 18 months old.

Publication dates and source age

Sources counted: 1

Newest dated source: 2022-03-04

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

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Prepared with AI assistance. Not independently fact-checked. Original sources and qualifications are below.

In plain language

Training that uses people's judgments to reward preferred model behavior. [1]

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Explanation based on the linked sources. Claim ID: claim-term-rlhf-1a5192ba3d7825ca26b24a72. Edition 0.20.0.

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Limits & distinctions

A preferred answer can still be wrong. The people giving feedback also cannot represent every user's values and needs. [1]

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Explanation based on the linked sources. Claim ID: claim-term-rlhf-09422ce4d5c74cb753a9bb98. Edition 0.20.0.

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A fuller explanation

In the InstructGPT approach, people compare candidate answers. Their choices train a separate reward model, which scores responses. Further training encourages the language model to produce higher-scoring answers. [1]

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Explanation based on the linked sources. Claim ID: claim-term-rlhf-1e4c26398ee834b2e16dc7b8. Edition 0.20.0.

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How it relates to the map

Ask who provided feedback, what instructions they received and which tasks they judged. [1]

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Editorial interpretation or qualification. Claim ID: claim-term-rlhf-25f52a21ad9f2e54a62ed1b2. Edition 0.20.0.

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https://theaiatlas.org/ideas/rlhf/

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Sources and what we read

  1. 1. Training language models to follow instructions with human feedback

    Long Ouyang and coauthors / arXiv · First-hand source · Published 2022-03-04 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2022-03-04

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read abstract, section 3.1's demonstrations/comparisons/reward-model procedure and section 5.3's limitations. Human preference judgments and improved results on the authors' tasks do not establish universal alignment or safety. Publication date checked on the arXiv abstract page.

    Source ID: instructgpt-paper

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

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