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Fair Policy Aggregation from Standard Policy Optimization

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
AI alignmentreinforcement learningdemocratic AI alignmentpluralistic AI alignmentcomputational social choice
Abstract:

Currently the most powerful AI systems are aligned with human values via reinforcement learning from human feedback. Yet, reinforcement learning from human feedback models human preferences as noisy samples from a single linear ordering of shared human values and is unable to incorporate democratic AI alignment. In particular, the standard approach fails to represent and reflect diverse and conflicting perspectives of pluralistic human values. Recent research introduced the theoretically principled notion of quantile fairness for training a reinforcement learning policy in the presence of multiple, competing sets of values from different agents. Quite recent work provided an algorithm for achieving quantile fairness in the tabular setting with explicit access to the full set of states, actions and transition probabilities in the MDP. These current methods require solving linear programs with the size of the constraint set given by the number of states and actions, making it unclear how to translate this into practical training algorithms that can only take actions and observe individual transitions from the current state. In this paper, we design and prove the correctness of a new algorithm for quantile fairness that makes efficient use of standard policy optimization as a black-box without any direct dependence on the number of states or actions. We further empirically validate our theoretical results and demonstrate that our algorithm achieves competitive fairness guarantees to the prior work, while being orders of magnitude more efficient with respect to computation and the required number of samples. Our algorithm opens a new avenue for provable fairness guarantees in any setting where standard policy optimization is possible.

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Overview

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
3
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Aggregating multiple reward functions in reinforcement learning for fair policy selection. The field addresses how agents can balance competing objectives—such as efficiency, equity, and stakeholder preferences—when a single scalar reward is insufficient. The taxonomy reveals six main branches that collectively span the landscape. Multi-Objective Reinforcement Learning Frameworks for Fairness and Theoretical Foundations and Algorithmic Innovations provide the conceptual backbone, developing Pareto-based methods (Pareto Fairness Utility[2], Multi-Objective Fair RL[1]) and welfare-theoretic approaches (Welfare Fairness MORL[7], Gini Welfare Functions[12]) that formalize trade-offs among objectives. Reward Function Design and Aggregation Techniques explores practical scalarization and weighting schemes, while Constrained Reinforcement Learning for Fairness enforces hard fairness constraints alongside primary objectives. Multi-Agent Reinforcement Learning with Fairness Considerations examines coordination and equity in decentralized settings (Kindness MARL[29], Fairness-Aware Cooperation DRL[46]), and Domain-Specific Applications of Fair Multi-Objective RL demonstrates these ideas in areas like traffic control, healthcare resource allocation, and recommendation systems (Multi-Objective RecSys Survey[3]). Recent work highlights tensions between computational scalability and fairness guarantees, with some studies pursuing large-scale deployments (Large-Scale Diffusion RL[6]) and others refining theoretical notions of equity under uncertainty (Scalable Lorenz Dominance[11], Group Fairness Multi-Objective[4]). Fair Policy Aggregation[0] sits within the Theoretical Foundations branch, contributing algorithmic mechanisms for combining multiple reward signals into policies that respect fairness criteria. It shares conceptual ground with Fair to Compromise[15] and Preference-Based Fairness[36], both of which also grapple with how to aggregate stakeholder utilities without imposing a single dominant objective. Where Fair to Compromise[15] emphasizes negotiation-style trade-offs and Preference-Based Fairness[36] incorporates explicit user preferences, Fair Policy Aggregation[0] focuses on principled aggregation rules that ensure no group is systematically disadvantaged. This positioning reflects broader debates about whether fairness should emerge from constraint satisfaction, welfare optimization, or transparent aggregation of diverse reward functions.

Claimed Contributions

Efficient algorithm for quantile-fair policy aggregation using policy optimization as black-box

The authors develop an algorithm that achieves quantile-fair policy aggregation by making O(n) calls to a policy optimization subroutine, avoiding explicit dependence on the MDP's state or action space size. This contrasts with prior methods requiring full access to transition probabilities.

0 retrieved papers
Optimal occupancy distribution for defining quantile fairness

The authors propose using a distribution over policies induced by individually optimal policies (the optimal occupancy distribution) rather than the uniform distribution over all policies. This choice enables tractable quantile estimation and avoids exponential sample complexity issues.

2 retrieved papers
Multiplicative weights update method for computing quantile-fair policies

The authors design an algorithm based on the multiplicative weights update method that computes quantile-fair policies efficiently, requiring only O(log n) policy evaluations instead of solving large linear programs over states and actions.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Efficient algorithm for quantile-fair policy aggregation using policy optimization as black-box

The authors develop an algorithm that achieves quantile-fair policy aggregation by making O(n) calls to a policy optimization subroutine, avoiding explicit dependence on the MDP's state or action space size. This contrasts with prior methods requiring full access to transition probabilities.

Contribution

Optimal occupancy distribution for defining quantile fairness

The authors propose using a distribution over policies induced by individually optimal policies (the optimal occupancy distribution) rather than the uniform distribution over all policies. This choice enables tractable quantile estimation and avoids exponential sample complexity issues.

Contribution

Multiplicative weights update method for computing quantile-fair policies

The authors design an algorithm based on the multiplicative weights update method that computes quantile-fair policies efficiently, requiring only O(log n) policy evaluations instead of solving large linear programs over states and actions.

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