AI RESEARCH
Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
arXiv CS.LG
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ArXi:2506.17252v4 Announce Type: replace Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its performance is highly dependent on the quality of the underlying human preference data. To address this bottleneck, prior work has explored various data selection strategies, but these methods often overlook the impact of the evolving states of the language model during the optimization process. In this paper, we