rlhf-small-datasets-reward-model-size

IN premise — summaries/2026/08/24/wiki-Reinforcement_learning_from_human_feedback-chunk-1.md

Created 2026-08-24T17:11:22+00:00

RLHF can be effective with relatively small amounts of comparison data, and proportionally increasing reward model size is often more beneficial than adding more annotation data.

Summary

You don't need a huge pile of human preference examples to train a reward model that works well for RLHF. In practice, spending your budget on a larger reward model usually gets you better results than spending it on collecting more human annotations.