Reward Model Accuracy Fails in Personalized Alignment
Published:Dec 28, 2025 20:27
•1 min read
•ArXiv
Analysis
This paper highlights a critical flaw in personalized alignment research. It argues that focusing solely on reward model (RM) accuracy, which is the current standard, is insufficient for achieving effective personalized behavior in real-world deployments. The authors demonstrate that RM accuracy doesn't translate to better generation quality when using reward-guided decoding (RGD), a common inference-time adaptation method. They introduce new metrics and benchmarks to expose this decoupling and show that simpler methods like in-context learning (ICL) can outperform reward-guided methods.
Key Takeaways
- •RM accuracy is a poor predictor of deployment performance in personalized alignment.
- •Reward-guided decoding (RGD) performance doesn't correlate well with RM accuracy.
- •New benchmarks and metrics are needed to evaluate personalized alignment effectively.
- •Simple methods like in-context learning can outperform reward-guided methods.
Reference
“Standard RM accuracy fails catastrophically as a selection criterion for deployment-ready personalized alignment.”