Training language models to follow instructions with human feedback history

Overview. The InstructGPT paper applies demonstrations and preference feedback to align model outputs with user intent.

Context. It reports that a smaller aligned model can be preferred to a much larger base model on the studied prompts.

Overview. The InstructGPT paper applies demonstrations and preference feedback to align model outputs with user intent.

Context. It reports that a smaller aligned model can be preferred to a much larger base model on the studied prompts.

How to use this research paper entry for Training language models to follow instructions with human feedback. Read the primary work for its stated setup and reported evidence, then distinguish what the authors demonstrated from what later summaries infer.

Evidence note. This Research research paper record for Training language models to follow instructions with human feedback is presented as an as-of-2026-07-13 orientation. The claim ledger identifies the source supporting its core statements; follow that record for the publication's full context and any later changes.

The InstructGPT paper applies demonstrations and preference feedback to align model outputs with user intent.

It reports that a smaller aligned model can be preferred to a much larger base model on the studied prompts.

Why it matters: Training language models to follow instructions with human feedback is best understood as part of a system, not as a label in isolation. The practical questions are what inputs it accepts, what outputs it produces, which evidence supports its capabilities, what constraints surround deployment, and how those details change over time. The Compendium separates the documented facts above from interpretation and records an as-of date so readers can re-check the primary source.

Reading note: this is original explanatory writing. It links to the cited source instead of reproducing protected text. Product availability, model names, pricing, and policies can change; follow the source for the provider's current position.

The InstructGPT paper applies demonstrations and preference feedback to align model outputs with user intent.

It reports that a smaller aligned model can be preferred to a much larger base model on the studied prompts.

Why it matters: Training language models to follow instructions with human feedback is best understood as part of a system, not as a label in isolation. The practical questions are what inputs it accepts, what outputs it produces, which evidence supports its capabilities, what constraints surround deployment, and how those details change over time. The Atlas separates the documented facts above from interpretation and records an as-of date so readers can re-check the primary source.

Reading note: this is original explanatory writing. It links to the cited source instead of reproducing protected text. Product availability, model names, pricing, and policies can change; follow the source for the provider's current position.