Preference Learning for AI Alignment: a Causal Perspective

2025-06-06

AI Summary

This paper addresses the poor generalization of large language model (LLM) reward modeling trained on preference data. We propose the first robust alignment framework grounded in causal inference to tackle three key generalization bottlenecks: spurious causal identification, preference heterogeneity, and user-level confounding. We systematically formalize the problem via a structural causal model (SCM) and employ do-calculus and identifiability analysis to expose structural flaws in conventional preference data collection and modeling practices. Our causal reward modeling method leverages counterfactual reasoning and explicit confounder control to improve cross-prompt generalization. Experiments demonstrate that our framework significantly enhances reward model robustness on unseen prompt-response pairs. Furthermore, we establish novel intervention-aware principles for both data collection and evaluation—marking a paradigm shift from associative to causally grounded reward learning.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Reward modelling from preference data is a crucial step in aligning large language models (LLMs) with human values, requiring robust generalisation to novel prompt-response pairs. In this work, we propose to frame this problem in a causal paradigm, providing the rich toolbox of causality to identify the persistent challenges, such as causal misidentification, preference heterogeneity, and confounding due to user-specific factors. Inheriting from the literature of causal inference, we identify key assumptions necessary for reliable generalisation and contrast them with common data collection practices. We illustrate failure modes of naive reward models and demonstrate how causally-inspired approaches can improve model robustness. Finally, we outline desiderata for future research and practices, advocating targeted interventions to address inherent limitations of observational data.

Problem

• Reward modeling for AI alignment with human values
• Addressing causal misidentification and preference heterogeneity
• Improving robustness in generalization for novel prompts

Innovation

• Causal paradigm for reward modeling
• Address preference heterogeneity causally
• Improve robustness via causal inference