MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Guangchen Lan, Sipeng Zhang, Tianle Wang, Yuwei Zhang, Daoan Zhang, Xinpeng Wei, Xiaoman Pan, Hongming Zhang, Dong-Jun Han, Christopher G. Brinton
2025-07-30
Summary
This paper talks about MaPPO, a new method that helps large language models better learn what humans prefer by using prior knowledge about rewards during training.
What's the problem?
The problem is that current methods for teaching AI to satisfy human preferences often treat the task as a simple positive or negative choice between responses, which can oversimplify and limit how well AI aligns with what people really want.
What's the solution?
MaPPO solves this by adding prior information about expected rewards into the training process, using a statistical approach called Maximum a Posteriori. This makes the learning process more nuanced, avoids oversimplifying preferences, and improves the AI’s performance, all without needing extra complex adjustments.
Why it matters?
This matters because it helps make AI models more reliable and better at understanding and following human preferences, leading to smarter and more useful AI assistants across many tasks.
Abstract
MaPPO, a framework for preference optimization, enhances alignment of large language models with human preferences by integrating prior reward knowledge into a Maximum a Posteriori objective, improving performance across various benchmarks.