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Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
arXiv – CS AI|Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao, Jujie He, Jiacai Liu, Chaojie Wang, Rui Yan, Wei Shen, Fuxiang Zhang, Jiacheng Xu, Yang Liu, Yahui Zhou||1 views
🤖AI Summary
Researchers introduce Skywork-Reward-V2, a suite of AI reward models trained on SynPref-40M, a massive 40-million preference pair dataset created through human-AI collaboration. The models achieve state-of-the-art performance across seven major benchmarks by combining human annotation quality with AI scalability for better preference learning.
Key Takeaways
- →SynPref-40M contains 40 million preference pairs created through a human-AI synergistic pipeline for training reward models.
- →Skywork-Reward-V2 includes eight models ranging from 0.6B to 8B parameters trained on 26 million curated preference pairs.
- →The models achieve state-of-the-art performance across seven major reward model benchmarks and outperform existing generative reward models.
- →Human-AI collaboration in data curation proves more effective than purely synthetic or human-only approaches for preference learning.
- →The research demonstrates that data quality through careful curation is as important as scale for reward model effectiveness.
#reinforcement-learning#reward-models#rlhf#preference-learning#human-ai-collaboration#dataset-curation#model-training#ai-alignment
Read Original →via arXiv – CS AI
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