Fine-tunes language models on human demonstrations and then on human preference comparisons collected through reinforcement learning. The paper is the origin of the RLHF pipeline that produced the first widely used instruction-following assistants.
@inproceedings{ouyang2022training,
author = {Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and others},
title = {Training Language Models to Follow Instructions with Human Feedback},
booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
year = {2022},
eprint = {2203.02155},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2203.02155}
}Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.
2026
This is the evaluation we run before we let an agent touch anything a customer can see. The finding that matters is the split — a model can pass direct prompt-injection tests and still fold the moment the same instruction comes back through a tool result. Those are different failure surfaces and one test does not cover both.
2026
This is the exact shape of a deployed support or negotiation agent: it works for the business and talks to someone who does not. The split the benchmark finds is the one that matters commercially, because the failure is not leaking data to the counterparty, it is refusing the principal so broadly that the agent stops doing the job it was hired for.
2026
Directly relevant to anyone bolting retrieval onto a model and assuming the citations are the safety mechanism. Adding a fake citation to a true claim made models worse, not better. Grounding that is not verified is worse than no grounding at all, because it arrives with the confidence of evidence.