Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as efficient alternatives to reinforcement learning from human feedback (RLHF), that do not require a separate reward model. However, the preference datasets used in DAP methods are usually collected ahead of training and never updated, thus the feedback is purely offline. Moreover, responses in these datasets are often sampled from a language model distinct from the one being aligned, and since the model evolves over training, the alignment phase is inevitably off-policy. In this study, we posit that online feedback is key and improves DAP methods. Our method, online AI feedback (OAIF), uses an LLM as annotator: on each training iteration, we sample two responses from the current model and prompt the LLM annotator to choose which one is preferred, thus providing online feedback. Despite its simplicity, we demonstrate via human evaluation in several tasks that OAIF outperforms both offline DAP and RLHF methods. We further show that the feedback leveraged in OAIF is easily controllable, via instruction prompts to the LLM annotator.

翻译：基于偏好的直接对齐方法（如DPO）近期成为强化学习从人类反馈（RLHF）的高效替代方案，这类方法无需独立的奖励模型。然而，DAP方法中使用的偏好数据集通常是在训练前收集且永不更新的，因此反馈完全属于离线形式。此外，这些数据集中的回答往往来自与被对齐模型不同的语言模型，且由于模型在训练过程中不断演化，对齐阶段不可避免地存在策略偏移问题。本研究提出在线反馈至关重要，能够改进DAP方法。我们的方法——在线AI反馈（OAIF）——采用大语言模型作为标注器：每次训练迭代中，我们从当前模型中采样两个回答，并提示大语言模型标注器选择更优的一个，从而提供在线反馈。尽管方法简洁，我们通过多项任务的人工评估证明OAIF优于离线DAP和RLHF方法。我们进一步证明OAIF中的反馈可通过向大语言模型标注器提供指令提示来轻松控制。