PAFO applies Pareto fairness optimization and group-specialized distillation to produce a single personalized reward model that improves accuracy for both majority and minority preference groups without requiring group labels at inference.
Multi-objective alignment of large language models through hypervolume maximization
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
MOCHA combines Chebyshev scalarization with exponential annealing to optimize LLM agent skills across performance and platform constraints, improving mean correctness by 7.5% over baselines on six tasks while finding more Pareto-optimal variants.
Focal Reward balances rubric-based RL by saturation-aware reweighting derived from inverse reward projection, outperforming static aggregation on 18 model-benchmark pairs.
citing papers explorer
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PAFO: Pareto Fairness Optimization for Personalized Reward Modeling
PAFO applies Pareto fairness optimization and group-specialized distillation to produce a single personalized reward model that improves accuracy for both majority and minority preference groups without requiring group labels at inference.
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MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
MOCHA combines Chebyshev scalarization with exponential annealing to optimize LLM agent skills across performance and platform constraints, improving mean correctness by 7.5% over baselines on six tasks while finding more Pareto-optimal variants.
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Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards
Focal Reward balances rubric-based RL by saturation-aware reweighting derived from inverse reward projection, outperforming static aggregation on 18 model-benchmark pairs.