arXiv cs.CLPaper
STAGE: Controlled Objective Admission for Multi-Preference LLM Alignment
The paper argues that how you order objectives during training matters more than existing methods assume. It's an optimization detail that shows measurable gains over naive multi-objective scalarization. For builders working on multi-value systems: this is the kind of contribution that might shave a few percentage points off training time or improve pareto-frontier coverage, but it's not going to change your architecture decisions.