A bias-aware Bayesian model with judge-specific covariates and a top-k membership uncertainty acquisition rule recovers accurate top-k rankings from noisy LLM judges using fewer comparisons than naive aggregation or standard active learning.
The Method of Paired Comparisons , author=
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MoR lets clients train local reward models on private preferences and uses a learned Mixture-of-Rewards with GRPO on the server to align a shared base VLM without exchanging parameters, architectures, or raw data.
DRRO for RLHF minimizes worst-case regret relative to the best policy under Wasserstein reward perturbations, yielding an exact inner solution and water-filling policy structure for the promptwise simplex model plus a practical policy-gradient algorithm.
citing papers explorer
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Ask the Right Comparison:Bias-Aware Bayesian Active Top-$k$ Ranking with LLM Judges
A bias-aware Bayesian model with judge-specific covariates and a top-k membership uncertainty acquisition rule recovers accurate top-k rankings from noisy LLM judges using fewer comparisons than naive aggregation or standard active learning.
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Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
DRRO for RLHF minimizes worst-case regret relative to the best policy under Wasserstein reward perturbations, yielding an exact inner solution and water-filling policy structure for the promptwise simplex model plus a practical policy-gradient algorithm.