dailyai.report

Stressful conditions during annotation create structured confounds in RLHF preference labels. These shifts differ from random noise, as rater distress encodes directly into reward models and policy optimization. The researchers propose a new audit framework to detect these state-dependent biases. Practitioners must now account for annotator wellbeing to ensure model alignment.