AI RESEARCH
Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba
arXiv CS.LG
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ArXi:2602.20730v2 Announce Type: replace We study efficiency as a first-class objective in Neural Combinatorial Optimization (NCO) and present ECO, an efficient learning framework that combines batched preference optimization with a Mamba backbone. Instead of tightly interleaving every policy update with on-policy rollouts, ECO decouples trajectory generation from gradient updates through two stages: supervised warm-up on pre-computed solutions and iterative Direct Preference Optimization (DPO) on batched candidate sets generated by the current policy.