Researchers propose GAP-DPO for personalized LLM alignment
A new paper on arXiv introduces GAP-DPO, a method designed to improve how large language models are personalized to individual user preferences. The authors argue that standard Direct Preference Optimization (DPO) often fails in personalized settings because it relies on heuristic pair selection that does not align with actual user utility. By analyzing the geometric relationship between user…
Key points
- GAP-DPO aligns preference pair selection with user utility gradients to improve personalization.
- Standard DPO heuristics can decouple optimization from user utility, degrading personalization results.
- Experiments show GAP-DPO improves stylistic fidelity and generation quality over standard DPO variants.
The proposed algorithm, GAP-DPO, uses an iterative approach to select preference pairs that are geometry-aligned with user utility. It also controls distribution shift through epoch-wise regeneration. The study notes that under off-policy sampling, DPO updates can shift from error correction to reinforcement-like behavior when preference margins align with utility gradients.
Experiments on personalized text generation benchmarks indicate that GAP-DPO improves stylistic fidelity, preference alignment, and overall generation quality compared to standard DPO variants. The findings suggest that treating pair selection as an intrinsic part of the optimization geometry is key to effective personalization.
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