Zilong Zheng
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Xetrieval introduces an embedding-level framework to mechanistically explain dense retrieval decisions by decomposing high-dimensional embeddings into sparse, human-interpretable features.
The paper introduces SAVE, a framework that uses on-policy feedback and the value function to self-supervise and improve reward models, significantly enhancing RLHF performance across multiple benchmarks.
Papers
The Flip Side of RLHF: On-Policy Feedback for Reward Model Self-Supervised Improvement
Xiaobo Wang, Tong Wu, Min Tang, Jiaqi Li +2 more
The paper introduces SAVE, a framework that uses on-policy feedback and the value function to self-supervise and improve reward models, significantly enhancing RLHF performance across multiple benchma…