SURFPRM is proposed, a graph-guided process preference synthesis framework for comparative Web PRMs that outperforms baseline-trained models on average on WEBPRMBENCH and rival leading proprietary LLMs, and elevates the GMCP proportion from 24.19% to 74.60%, producing the curated SURFPRM-DATA dataset.
Yang-Zhe Peng, Xiao-Yang Wang, Yi-Yang Zhao et al.· 0 citations
R-Select, a robust and scalable framework that optimizes data selection with 30 distinct quality metrics, introduces a novel hierarchical optimization strategy that consistently outperforms both heuristic baselines and model-based methods, offering a robust solution for high-quality data curation.
Xin Gao, Xiao-Yang Wang, Yun Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes DOTA, a data selection framework that minimizes the cost of generating preference data, while still ensuring the quality of training, and proposes a theoretically grounded metric called Preference Perplexity (PFP) that enables it to design a low cost, gradient-based method to effectively estimate the...
Chi Zhang, Jia-Chen T. Wang, Kun He et al.· Proceedings of the VLDB Endo...· 0 citations
The transition from architecture-centric scaling to data-centric refinement has established high-quality data as a critical determinant of Large Language Model performance, particularly for complex reasoning and instruction following. However, effective data selection remains a persistent bottleneck: simple heuristic f...
Xin Gao, Xiaoyang Wang, Yun Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
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