Reinforcement learning from human feedback (RLHF) has emerged as the main
paradigm for aligning large language models (LLMs) with human preferences.
Typically, RLHF involves the initial step of learning a reward model from human
feedback, often expressed as preferences between pairs of text generations
produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by
optimizing it to maximize the reward model through a reinforcement learning
algorithm. However, an inherent limitation of current reward models is their
inability to fully represent the richness of human preferences and their
dependency on the sampling distribution.
In this study, we introduce an alternative pipeline for the fine-tuning of
LLMs using pairwise human feedback. Our approach entails the initial learning
of a preference model, which is conditioned on two inputs given a prompt,
followed by the pursuit of a policy that consistently generates responses
preferred over those generated by any competing policy, thus defining the Nash
equilibrium of this preference model. We term this approach Nash learning from
human feedback (NLHF).
In the context of a tabular policy representation, we present a novel
algorithmic solution, Nash-MD, founded on the principles of mirror descent.
This algorithm produces a sequence of policies, with the last iteration
converging to the regularized Nash equilibrium. Additionally, we explore
parametric representations of policies and introduce gradient descent
algorithms for deep-learning architectures. To demonstrate the effectiveness of
our approach, we present experimental results involving the fine-tuning of a
LLM for a text summarization task. We believe NLHF offers a compelling avenue
for preference learning and policy optimization with the potential of advancing
the field of aligning LLMs with human preferences.

通过带有人类反馈的强化学习，我们引入一种新的方法来提升大型语言模型的性能，通过学习人类偏好并优化策略，实现与人类偏好的协调。