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

Andreas Chouliaras, Dimitris Chatzopoulos

TL;DR

We address inefficiency in RLHF preference learning caused by random sampling paired with the Bradley-Terry model, especially under tight annotation budgets. The authors compare several sampling and aggregation strategies and introduce Swiss InfoGain, a Swiss-tournament-based method with mutual-information gain pairing, showing strong data efficiency in low-resource regimes and competitive performance at higher budgets. Across synthetic experiments with 100 items, Swiss InfoGain often outperforms BT and Copeland-based methods at limited budgets, while Borda-Copeland can dominate with abundant data, highlighting a need for resource-aware RLHF pipelines. The study demonstrates that adaptive, information-theoretic pairing strategies can substantially reduce human annotation burden while maintaining or improving alignment quality.

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.

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

TL;DR

We address inefficiency in RLHF preference learning caused by random sampling paired with the Bradley-Terry model, especially under tight annotation budgets. The authors compare several sampling and aggregation strategies and introduce Swiss InfoGain, a Swiss-tournament-based method with mutual-information gain pairing, showing strong data efficiency in low-resource regimes and competitive performance at higher budgets. Across synthetic experiments with 100 items, Swiss InfoGain often outperforms BT and Copeland-based methods at limited budgets, while Borda-Copeland can dominate with abundant data, highlighting a need for resource-aware RLHF pipelines. The study demonstrates that adaptive, information-theoretic pairing strategies can substantially reduce human annotation burden while maintaining or improving alignment quality.

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.

Paper Structure

This paper contains 17 sections, 6 equations, 6 figures, 6 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Methodology
  4. The Bradley-Terry estimator.
  5. The Borda count method.
  6. The Elo rating system
  7. The Swiss tournament method
  8. Random pairs then Swiss Tournament.
  9. Mutual information gain pairs in Swiss tournament.
  10. Experimental Evaluation
  11. Comparing methods given a fixed annotation budget $M$
  12. Comparing the method performance as $M$ increases
  13. Discussion
  14. Limitations and comparison to related work
  15. Conclusion
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Elo change based on initial rating difference for various K-Factor values.
  • Figure 2: The Copeland pairing algorithm.
  • Figure 3: The Swiss tournament system.
  • Figure 4: Probabilities of comparison outcome based on value difference
  • Figure 5: Correlation of Estimated value vs True value.
  • ...and 1 more figures