Training language models to follow instructions with human feedback
Human demonstrations and preference feedback are used to improve how a language model follows instructions.
An open-source toolkit uses language models to help turn qualitative text and image data into measurable variables.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Read the methodology and validation requirements before treating model-generated measurements as evidence.
@misc{team2026scaling,
title = {Scaling social science research},
author = {OpenAI Economic Research Team},
year = {2026},
url = {https://openai.com/index/scaling-social-science-research/}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
Human demonstrations and preference feedback are used to improve how a language model follows instructions.
A set of written principles guides critique, revision and AI-generated preference feedback during alignment.
A preference-based objective optimizes a language model directly, avoiding a separate reward-model training stage.