neural-combinatorial-optimization

Introduces Evolving Programmatic Bottlenecks (EPB), a framework for interpreting neural combinatorial optimization policies by distilling black-box models into human-readable program portfolios using LLM-guided evolution.

This paper proposes Node-Edge Policy Factorization (NEPF) to address scalability issues in solving Vehicle Routing Problems on multigraphs. It combines pre-encoding edge aggregation with a hierarchical reinforcement learning method to achieve state-of-the-art solution quality with faster training and inference.