arXiv Machine Learning July 21, 2026 By Zhenxing Xu, Zeyuan Ma, Weidong Bao, Yan Zheng, Chongshuang Hu, Ji Wang, Zhiguang Cao

Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba

Read the original on arXiv Machine Learning →

arXiv:2602. 20730v3 Announce Type: replace Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

arXiv:2510. 05342v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models.

By Hyung Gyu Rho

llms nlp reinforcement-learning

arXiv Machine Learning

Jul 28

WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems

arXiv:2605. 22876v2 Announce Type: replace Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize a MOCOP into multiple subproblems associated with distinct weight vectors.

By Xuan Wu, Jinbiao Chen, Yang Li, Lijie Wen, Chunguo Wu, Yuanshu Li, Yubin Xiao, Chunyan Miao, You Zhou, Di Wang

benchmarks

arXiv AI

Jun 10

Baseline-Free Policy Optimization for Neural Combinatorial Optimization

arXiv:2606. 10321v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems.

By Carlos S. Sep\'ulveda, Gonzalo A. Ruz

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 31

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.

By Shengda Gu, Kai Li, Xinyi Ke, Haobo Fu, Yifan Zhang, Jian Cheng

llms reinforcement-learning

arXiv AI

Jun 2

Value-Free Policy Optimization via Reward Partitioning

arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.

By Bilal Faye, Hanane Azzag, Mustapha Lebbah

llms reinforcement-learning fine-tuning

Hugging Face Trending Papers

Jun 1

Drifting Preference Optimization for One-Step Generative Models

One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization. We propose Drifting Preference Optimization (DrPO), an online preference-finetuning method for deterministic one-step generators.

diffusion fine-tuning benchmarks safety

Related stories

Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems

Baseline-Free Policy Optimization for Neural Combinatorial Optimization

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

Value-Free Policy Optimization via Reward Partitioning

Drifting Preference Optimization for One-Step Generative Models