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SOLID is proposed, a novel framework for self-improving OR language models without verified answers or external evaluators that improves solution accuracy for both general-purpose and OR-tuned models over outcome-only group-relative training.
This work introduces OR-Clarify, a benchmark for pre-formulation clarification and proposes Interactive Optimization (InterOPT), a two-stage framework that identifies unresolved formulation-critical gaps and uses them to guide whether to ask the next question or to stop.
GraphBU is proposed, a graph-native generator whose basic unit is a local subproblem plus its interface whose basic unit is a local subproblem plus its interface that keeps graph statistics close to the source family, preserves feasibility on most datasets, and improves downstream Predict-and-Search training.
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