Скачать или смотреть BIS: Training Efficient MLLM Reward Models

In this AI Research Roundup episode, Alex discusses the paper: 'Training Data Efficiency in Multimodal Process Reward Models' Multimodal Process Reward Models (MPRMs) provide critical step-level supervision for Multimodal Large Language Models but are often limited by massive, redundant datasets. This research identifies that MPRM performance saturates quickly because much of the existing training data does not contribute to model improvement. The authors propose a new theoretical framework focused on label mixture and label reliability to filter out high-noise samples. Based on these insights, they introduce the Balanced-Information Score (BIS) to select the most informative data for training. This method significantly improves efficiency by focusing on high-quality, reliable reasoning steps. Paper URL: https://arxiv.org/abs/2602.04145 #AI #MachineLearning #DeepLearning #MLLM #MPRM #DataEfficiency

Resources:
GitHub: https://github.com/JinYuanLi0012/Bala...