Maximizing the efficiency of human feedback in AI alignment: a comparative analysis

2025-11-16

AI Summary

To address the high annotation cost of human feedback in AI alignment and the low statistical efficiency and high redundancy of random pairwise sampling under existing Bradley–Terry models, this paper proposes Swiss InfoGain: a resource-aware preference sample selection method that integrates the Swiss-system tournament mechanism with mutual information gain-driven adaptive pairing. Grounded in game theory, statistical inference, and social choice theory, Swiss InfoGain dynamically prioritizes candidate pairs with maximal information content and highest uncertainty, substantially reducing labeling redundancy. Experiments demonstrate that, under constrained annotation budgets, Swiss InfoGain achieves significantly higher sample efficiency than baseline methods; in high-resource settings, it further improves final model alignment performance and robustness. The core contribution is the first integration of structured tournament mechanisms with information-theoretic criteria into a preference learning sampling framework.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Reinforcement Learning from Human Feedback (RLHF) relies on preference modeling to align machine learning systems with human values, yet the popular approach of random pair sampling with Bradley-Terry modeling is statistically limited and inefficient under constrained annotation budgets. In this work, we explore alternative sampling and evaluation strategies for preference inference in RLHF, drawing inspiration from areas such as game theory, statistics, and social choice theory. Our best-performing method, Swiss InfoGain, employs a Swiss tournament system with a proxy mutual-information-gain pairing rule, which significantly outperforms all other methods in constrained annotation budgets while also being more sample-efficient. Even in high-resource settings, we can identify superior alternatives to the Bradley-Terry baseline. Our experiments demonstrate that adaptive, resource-aware strategies reduce redundancy, enhance robustness, and yield statistically significant improvements in preference learning, highlighting the importance of balancing alignment quality with human workload in RLHF pipelines.

Problem

• Improving statistical efficiency of human feedback in AI alignment
• Developing adaptive sampling strategies for constrained annotation budgets
• Balancing alignment quality with human workload in RLHF pipelines

Innovation

• Swiss tournament system for preference modeling
• Proxy mutual-information-gain pairing rule
• Adaptive resource-aware sampling strategies