VAGPO: Vision-augmented Asymmetric Group Preference Optimization for the Routing Problems

• URL: http://arxiv.org/abs/2508.01774v1
• Date: Sun, 03 Aug 2025 14:19:12 GMT
• Title: VAGPO: Vision-augmented Asymmetric Group Preference Optimization for the Routing Problems
• Authors: Shiyan Liu, Bohan Tan, Yan Jin,
• Abstract summary: We propose a novel Vision-Augmented Asymmetric Group Preference Optimization (VAGPO) approach for solving the routing problems.<n>By leveraging ResNet-based visual encoding and Transformer-based sequential modeling, VAGPO captures both spatial structure and temporal dependencies.<n> Experimental results show that the proposed VAGPO not only achieves highly competitive solution quality but also exhibits strong generalization to larger instances without re-training.
• Score: 2.150410718150006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The routing problems such as the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) are well-known combinatorial optimization challenges with broad practical relevance. Recent data-driven optimization methods have made significant progress, yet they often face limitations in training efficiency and generalization to large-scale instances. In this paper, we propose a novel Vision-Augmented Asymmetric Group Preference Optimization (VAGPO) approach for solving the routing problems. By leveraging ResNet-based visual encoding and Transformer-based sequential modeling, VAGPO captures both spatial structure and temporal dependencies. Furthermore, we introduce an asymmetric group preference optimization strategy that significantly accelerates convergence compared to commonly used policy gradient methods. Experimental results on TSP and CVRP benchmarks show that the proposed VAGPO not only achieves highly competitive solution quality but also exhibits strong generalization to larger instances (up to 1000 nodes) without re-training, highlighting its effectiveness in both learning efficiency and scalability.

Related papers

• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization [12.899626317088885]
  This paper introduces LCC, a pioneering learning-based cooperative coevolution framework.<n> LCC dynamically schedules decomposition strategies during optimization processes.<n>It offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption.
  arXiv  Detail & Related papers  (2025-04-24T14:09:22Z)
• Gradient Correction in Federated Learning with Adaptive Optimization [19.93709245766609]
  We propose tt FAdamGC, the first algorithm to integrate client-drift compensation into adaptive optimization.<n>We show that tt FAdamGC consistently outperforms existing methods in total communication and cost across varying levels of data.
  arXiv  Detail & Related papers  (2025-02-04T21:21:30Z)
• A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization [5.757318591302855]
  We propose a RankNet-Inspired Surrogate-assisted Hybrid Metaheuristic to handle large-scale coverage optimization tasks.<n>Our algorithm consistently outperforms state-of-the-art algorithms for EMVOPs.
  arXiv  Detail & Related papers  (2025-01-13T14:49:05Z)
• Primitive Agentic First-Order Optimization [0.0]
  This work presents a proof-of-concept study combining primitive state representations and agent-environment interactions as first-order reinforcement learning. The results show that elementary RL methods combined with succinct partial state representations can be used as optimizeds manage complexity in RL-based optimization.
  arXiv  Detail & Related papers  (2024-06-07T11:13:38Z)
• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer [52.09480867526656]
  We identify the source of misalignment as a form of distributional shift and uncertainty in learning human preferences.<n>To mitigate overoptimization, we first propose a theoretical algorithm that chooses the best policy for an adversarially chosen reward model.<n>Using the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines a preference optimization loss and a supervised learning loss.
  arXiv  Detail & Related papers  (2024-05-26T05:38:50Z)
• Controllable Prompt Tuning For Balancing Group Distributional Robustness [53.336515056479705]
  We introduce an optimization scheme to achieve good performance across groups and find a good solution for all without severely sacrificing performance on any of them. We propose Controllable Prompt Tuning (CPT), which couples our approach with prompt-tuning techniques. On spurious correlation benchmarks, our procedures achieve state-of-the-art results across both transformer and non-transformer architectures, as well as unimodal and multimodal data.
  arXiv  Detail & Related papers  (2024-03-05T06:23:55Z)
• Towards Efficient Exact Optimization of Language Model Alignment [93.39181634597877]
  Direct preference optimization (DPO) was proposed to directly optimize the policy from preference data. We show that DPO derived based on the optimal solution of problem leads to a compromised mean-seeking approximation of the optimal solution in practice. We propose efficient exact optimization (EXO) of the alignment objective.
  arXiv  Detail & Related papers  (2024-02-01T18:51:54Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Learning Vehicle Routing Problems using Policy Optimisation [4.093722933440819]
  State-of-the-art approaches learn a policy using reinforcement learning, and the learnt policy acts as a pseudo solver. These approaches have demonstrated good performance in some cases, but given the large search space typical of routing problem, they can converge too quickly to poor policy. We propose entropy regularised reinforcement learning (ERRL) that supports exploration by providing more policies.
  arXiv  Detail & Related papers  (2020-12-24T14:18:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.