  Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement
learning from human feedback (RLHF) - where human preference judgments on LM
outputs are transformed into a learning signal - has recently shown promise in
addressing these issues. However, such holistic feedback conveys limited
information on long text outputs; it does not indicate which aspects of the
outputs influenced user preference; e.g., which parts contain what type(s) of
errors. In this paper, we use fine-grained human feedback (e.g., which sentence
is false, which sub-sentence is irrelevant) as an explicit training signal. We
introduce Fine-Grained RLHF, a framework that enables training and learning
from reward functions that are fine-grained in two respects: (1) density,
providing a reward after every segment (e.g., a sentence) is generated; and (2)
incorporating multiple reward models associated with different feedback types
(e.g., factual incorrectness, irrelevance, and information incompleteness). We
conduct experiments on detoxification and long-form question answering to
illustrate how learning with such reward functions leads to improved
performance, supported by both automatic and human evaluation. Additionally, we
show that LM behaviors can be customized using different combinations of
fine-grained reward models. We release all data, collected human feedback, and
codes at https://FineGrainedRLHF.github.io.
