DynamicPO: New Framework Addresses Preference Optimization Collapse in LLM-Based Recommendation Systems
Researchers have identified a phenomenon called preference optimization collapse in large language model-based recommendation systems, where adding more negative training samples paradoxically degrades performance despite lower training loss. The problem stems from gradient suppression caused by easily discriminable negatives overwhelming boundary-critical negatives that define user preferences. The team proposes DynamicPO, a lightweight framework using dynamic boundary negative selection and dual-margin adjustment to prevent collapse and improve recommendation accuracy.
A new preprint from arXiv describes a counterintuitive problem in LLM-based recommendation systems using direct preference optimization (DPO): increasing negative samples in training can worsen performance even as training loss decreases. Through empirical analysis and theoretical demonstration, researchers identified that this preference optimization collapse results from gradient suppression, where easily discriminable negatives dominate the optimization process and suppress signals from boundary-critical negatives that truly define user preference boundaries. This causes under-optimization of boundary-relevant signals and weakens the model's decision boundary. To address this, the authors propose DynamicPO, a plug-and-play framework featuring Dynamic Boundary Negative Selection to identify and prioritize informative negatives near the decision boundary, and Dual-Margin Dynamic Beta Adjustment to calibrate optimization strength per sample. Experiments on three public datasets demonstrate that DynamicPO effectively prevents optimization collapse with negligible computational overhead.
What's missing
The study's own limitations and open questions are not detailed in the abstract provided. Specific details about the three public datasets used for evaluation and comparative performance metrics against baseline methods are not included in the abstract.
What different sources said
- arXiv cs.AICenter
DynamicPO: Dynamic Preference Optimization for Recommendation
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