StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments. Traditional learning-based automated optimization modeling methods improve modeling policies through large-scale annotated or curated training data, but are costly to adapt to new problem distribu