UARM equips reward models with quantile-based conformal prediction uncertainty and reweights GRPO advantages via heteroscedastic variance decomposition to improve calibration and reduce reward hacking in RLHF.
Causalrm: Causal- theoretic reward modeling for rlhf from observational user feedbacks.arXiv preprint arXiv:2603.18736,
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Uncertainty-Aware Reward Modeling for Stable RLHF
UARM equips reward models with quantile-based conformal prediction uncertainty and reweights GRPO advantages via heteroscedastic variance decomposition to improve calibration and reduce reward hacking in RLHF.