  Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment
of large language models with human preferences, significantly enhancing the
quality of interactions between humans and models. InstructGPT implements RLHF
through several stages, including Supervised Fine-Tuning (SFT), reward model
training, and Proximal Policy Optimization (PPO). However, PPO is sensitive to
hyperparameters and requires multiple models in its standard implementation,
making it hard to train and scale up to larger parameter counts. In contrast,
we propose a novel learning paradigm called RRHF, which scores sampled
responses from different sources via a logarithm of conditional probabilities
and learns to align these probabilities with human preferences through ranking
loss. RRHF can leverage sampled responses from various sources including the
model responses from itself, other large language model responses, and human
expert responses to learn to rank them. RRHF only needs 1 to 2 models during
tuning and can efficiently align language models with human preferences
robustly without complex hyperparameter tuning. Additionally, RRHF can be
considered an extension of SFT and reward model training while being simpler
than PPO in terms of coding, model counts, and hyperparameters. We evaluate
RRHF on the Helpful and Harmless dataset, demonstrating comparable alignment
performance with PPO by reward model score and human labeling. Extensive
experiments show that the performance of RRHF is highly related to sampling
quality which suggests RRHF is a best-of-n learner. Codes available at
https://github.com/GanjinZero/RRHF.
