arXiv:2606.27578v1 Announce Type: new Abstract: Reward models for Reinforcement Learning from Human Feedback (RLHF) pool preferences across thousands of annotators and fit one global affine calibrator, collapsing raters with systematically different rating-scale offsets and slopes into a single average-rater fit that does not match any individual annotator. PEBS is a per-rater empirical-Bayes shrinkage estimator: it fits per-rater affine calibrators on a held-out slice of each annotator's ratings and applies Morris-James-Stein empirical-Bayes shrinkage toward the population mean, in closed for
The increasing sophistication and scale of RLHF models necessitate more robust and accurate calibration methods to address inherent biases from thousands of annotators.
Improved reward model calibration directly impacts the safety, reliability, and performance of large language models and other AI systems, enhancing their utility and trustworthiness.
The ability to accurately model individual Rater biases, moving beyond a single average-rater fit, will lead to more precise and less biased AI training.
- · AI developers
- · AI safety researchers
- · Large language model users
- · Companies implementing AI agents
- · AI models relying on uncalibrated or poorly calibrated human feedback
RLHF systems become more accurate and robust due to better handling of human feedback variability.
This improvement in AI system reliability accelerates adoption of AI agents in sensitive applications.
Enhanced AI agent capabilities could lead to more profound transformations in white-collar workflows, potentially impacting employment structures.
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Read at arXiv cs.LG