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Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Framework

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
AI AlignmentPopulation-Proportional AlignmentSocial Choice TheoryAxiomatic FrameworkRank AggregationPluralistic AlignmentPreference-based Reinforcement LearningReinforcement Learning from Human FeedbackNash Learning from Human FeedbackLarge Language Model
Abstract:

Conventional preference learning methods often prioritize opinions held more widely when aggregating preferences from multiple evaluators. This may result in policies that are biased in favor of some types of opinions or groups and susceptible to strategic manipulation. To address this issue, we develop a novel preference learning framework capable of aligning aggregate opinions and policies proportionally with the true population distribution of evaluator preferences. Grounded in social choice theory, our approach infers the feasible set of evaluator population distributions directly from pairwise comparison data. Using these estimates, the algorithm constructs a policy that satisfies foundational axioms from social choice theory, namely monotonicity and Pareto efficiency, as well as our newly-introduced axioms of population-proportional alignment and population-bounded manipulability. Moreover, we propose a soft-max relaxation method that smoothly trade-offs population-proportional alignment with the selection of the Condorcet winner (which beats all other options in pairwise comparisons). Finally, we validate the effectiveness and scalability of our approach through experiments on both tabular recommendation tasks and large language model alignment.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a preference learning framework grounded in social choice axioms—monotonicity, Pareto efficiency, and two newly proposed axioms for population-proportional alignment and bounded manipulability. It resides in the 'Social Choice Theory Foundations' leaf alongside one sibling paper (Axioms AI Alignment). This leaf is part of a small taxonomy (nine papers total across seven leaves), indicating a relatively sparse research area. The framework infers feasible population distributions from pairwise comparisons and constructs policies satisfying these axioms, positioning itself as a foundational contribution rather than an algorithmic or application-focused study.

The taxonomy reveals neighboring work in 'Population Distribution Inference' (one paper on inferring distributions directly from comparisons) and 'Multi-Reward and Pluralistic Alignment' (two papers on learning distributions over reward functions). The paper's emphasis on axiomatic guarantees distinguishes it from these neighbors: the distribution inference leaf focuses on estimation methods without explicit axioms, while the pluralistic alignment leaf addresses diversity through multi-reward frameworks rather than formal social choice principles. The taxonomy's scope notes clarify that axiomatic grounding is the defining boundary separating this work from heterogeneity-focused methods.

Among thirty candidates examined, none clearly refuted any of the three contributions. The population-proportional alignment framework (ten candidates examined, zero refutable) and the two new axioms (ten candidates, zero refutable) appear novel within the limited search scope. The softmax relaxation method balancing proportionality with Condorcet consistency (ten candidates, zero refutable) also shows no direct prior overlap. These statistics suggest the work introduces concepts not prominently represented in the top-thirty semantic matches, though the search scope does not cover the entire field exhaustively.

Given the sparse taxonomy structure and absence of refutable prior work among examined candidates, the paper appears to occupy a relatively unexplored niche at the intersection of social choice theory and preference learning. The limited search scope (thirty candidates) and small taxonomy (nine papers) mean this assessment reflects local novelty rather than a comprehensive field survey. Broader literature beyond semantic similarity may contain related axiomatic frameworks not captured here.

Taxonomy

Core-task Taxonomy Papers
9
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: population-proportional alignment of preferences from pairwise comparisons. This field addresses how to aggregate diverse human preferences—typically expressed through pairwise judgments—into models or policies that reflect the distribution of views in a population rather than imposing a single majority consensus. The taxonomy organizes work into several main branches: Theoretical Foundations and Axiomatic Frameworks establish formal guarantees and desiderata for fair aggregation (e.g., Axioms AI Alignment[2], Population Proportional Preference[1]); Heterogeneity and Pluralism in Preferences explore methods that explicitly model subgroup diversity and personalized reward structures (e.g., PAL Pluralistic Alignment[3], Personalized Preference Diffusion[4]); Algorithmic Methods for Robust Aggregation develop practical techniques for learning from noisy or miscalibrated comparisons (e.g., Pairwise Calibrated Rewards[5], Sign Estimator[8]); and Application Domains demonstrate these ideas in real-world settings such as cross-cultural value elicitation (e.g., DEVINE India[6]) or proportional representation in AI systems (e.g., Proportional Representation AI[7]). Together, these branches reflect a shift from winner-takes-all optimization toward fairness-aware, pluralistic alignment. A particularly active line of work focuses on bridging axiomatic social choice principles with scalable machine learning pipelines, asking how to operationalize notions like proportionality when preferences are high-dimensional or context-dependent. Another contrasting theme is the tension between personalization—tailoring outputs to individual or subgroup tastes—and population-level fairness, where no minority is systematically ignored. Population Proportional Alignment[0] sits squarely within the Theoretical Foundations and Axiomatic Frameworks branch, sharing conceptual ground with Axioms AI Alignment[2] in its emphasis on formal desiderata. Compared to more algorithm-focused neighbors like Pairwise Calibrated Rewards[5], it prioritizes establishing what properties a proportional aggregation scheme should satisfy, rather than detailing a specific training procedure. This positioning highlights an ongoing dialogue between principled theory and practical implementation across the taxonomy.

Claimed Contributions

Population-proportional alignment framework with axiomatic guarantees

The authors propose a preference learning framework that infers feasible evaluator population distributions from pairwise comparison data and constructs policies satisfying foundational axioms (monotonicity and Pareto efficiency) plus two newly-introduced axioms: population-proportional alignment and population-bounded manipulability.

10 retrieved papers
Two new axioms for preference learning

The paper introduces population-proportional alignment (PPA), which requires policies to be at least weakly proportional to evaluator population shares, and population-bounded manipulability (PBM), which bounds manipulation incentives as an affine function of true population share, addressing insufficient representation and robustness issues in existing methods.

10 retrieved papers
Softmax relaxation method for balancing proportionality and Condorcet consistency

The authors develop a softmax-based relaxation technique controlled by parameter beta that enables a smooth trade-off between achieving population-proportional alignment and selecting the Condorcet winner (the alternative that beats all others in pairwise comparisons).

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Population-proportional alignment framework with axiomatic guarantees

The authors propose a preference learning framework that infers feasible evaluator population distributions from pairwise comparison data and constructs policies satisfying foundational axioms (monotonicity and Pareto efficiency) plus two newly-introduced axioms: population-proportional alignment and population-bounded manipulability.

Contribution

Two new axioms for preference learning

The paper introduces population-proportional alignment (PPA), which requires policies to be at least weakly proportional to evaluator population shares, and population-bounded manipulability (PBM), which bounds manipulation incentives as an affine function of true population share, addressing insufficient representation and robustness issues in existing methods.

Contribution

Softmax relaxation method for balancing proportionality and Condorcet consistency

The authors develop a softmax-based relaxation technique controlled by parameter beta that enables a smooth trade-off between achieving population-proportional alignment and selecting the Condorcet winner (the alternative that beats all others in pairwise comparisons).

Table of Contents