Learning to Prefer Reliably: Error-Augmented Emotion Preference Optimization with Calibrated Fusion

Emotion preference learning uses pairwise comparisons between candidate descriptions to align multimodal large language models (MLLMs) with human judgments of open-ended emotion descriptions and to train reward models that capture human emotional preferences. However, conventional pairwise supervisi