通过逻辑理解直接偏好对齐的逻辑
Understanding the Logic of Direct Preference Alignment through Logic
AI总结：
本文通过将直接偏好对齐损失形式化为离散推理问题，提出一种新形式化方法，以表征常见DPA变体并探索损失景观，从而系统推导新损失函数，助力人类AI对齐。
摘要：
近期的直接偏好对齐算法（DPA），如DPO，在将大型语言模型与人类偏好对齐方面展现出了巨大潜力。尽管这推动了原始DPO损失函数的许多新变体的开发，但由于缺乏用于推理这些算法底层语义的技术和概念框架，理解这些近期提案之间的差异以及开发新的DPA损失函数仍然困难。在本文中，我们尝试通过将DPA损失形式化为离散推理问题来弥补这一不足。具体而言，我们提出一个问题：给定一个现有的DPA损失，我们能否系统地推导出一个表征其语义的符号程序？我们提出了一种新的形式化方法，用于表征基于单模型和参考模型的方法的偏好损失，并识别了多种常用DPA变体的符号形式。此外，我们展示了这种偏好学习的形式化视角如何为DPA损失景观的规模和结构提供新的见解，从而不仅能够严格表征近期损失提案之间的关系，还能系统地探索该景观并从第一性原理推导出新的损失函数。我们希望我们的框架和发现能为从事人类AI对齐工作的人们提供有用的指导。
英文摘要：
Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many new variants of the original DPO loss, understanding the differences between these recent proposals, as well as developing new DPA loss functions, remains difficult given the lack of a technical and conceptual framework for reasoning about the underlying semantics of these algorithms. In this paper, we attempt to remedy this by formalizing DPA losses in terms of discrete reasoning problems. Specifically, we ask: Given an existing DPA loss, can we systematically derive a symbolic program that characterizes its semantics? We propose a novel formalism for characterizing preference losses for single model and reference model based approaches, and identify symbolic forms for a number of commonly used DPA variants. Further, we show how this formal view of preference learning sheds new light on both the size and structure of the DPA loss landscape, making it possible to not only rigorously characterize the relationships between recent loss proposals but also to systematically explore the landscape and derive new loss functions from first principles. We hope our framework and findings will help provide useful guidance to those working on human AI alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
