Test-Time Alignment via Hypothesis Reweighting

ArXi:2412.08812v2 Announce Type: replace Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning or long-context conditioning are too costly for real-time personalization. We propose Hypothesis Reweighting (HyRe), which enables real-time personalization by reweighting ensemble members using just 1-5 labeled examples from the target user or domain.