arXiv Machine Learning October 3, 2026 By Changliang Zhou, Yuanyao Chen, Rongsheng Chen, Zhiyun Lin, Zhenkun Wang

MiLoop: Selective Memory Propagation for Neural Combinatorial Optimization

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MiLoop is a reinforcement‑learning‑based constructive framework for neural combinatorial optimization that propagates selective memory across rollout steps. By fusing current embeddings with historical memory before attention layers and applying adaptive gated updates afterward, it enables a shallow policy to learn dynamic embeddings without external solution labels or search‑space pruning. Experiments on four combinatorial optimization problems show MiLoop consistently generates high‑quality solutions for instances ranging from 100 to 10 million nodes, demonstrating strong generalization.

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