Title: Towards Generalized Combinatorial Solvers via Reward Adjustment Policy Optimization. Abstract: Recent reinforcement learning approaches have achieved impressive success in solving combinatorial optimization (CO) problems. However, most existing works focus on evaluating their solvers under a prevalent fixed-size protocol, ignoring generalization to differentRecent reinforcement learning approaches have achieved impressive success in solving combinatorial optimization (CO) problems. However, most existing works focus on evaluating their solvers under a prevalent fixed-size protocol, ignoring generalization to different-size instances. When the solver is confronted with instances of the size it has not been trained on, the performance drops dramatically. In practice, these approaches that lack size-insensitive generalization capacities are unacceptable since an additional training period is repeated for each new instance size. We observe the main obstacle preventing us from training a generalized combinatorial solver is oscillating reward signals. Reward oscillation mainly includes two sides: 1) The conventional reward fails to depict the actual performance of solvers for different instance sizes. 2) The inherent difficulties varying across different sizes worsen training stability. Thus, we present Reward Adjustment Policy Optimization (RAPO), an end-to-end approach to building combinatorial solvers for a wide range of CO problems. RAPO contains a reward adjustment method across instances with variable sizes to address the first side of reward oscillation, along with a promising curriculum strategy to alleviate another side. We conduct experiments on three popular CO problems, namely, the traveling salesman problem (TSP), the capacitated vehicle routing problem (CVRP), and the 0-1 knapsack problem (KP). RAPO exhibits significant improvement in generalization to instances with variable sizes consistently on all benchmarks. Remarkably, RAPO even outperforms its fixed-size counterparts in its well-trained size by a clear margin. size instances. When the solver is confronted with instances of the size it has not been trained on, the performance drops dramatically. In practice, these approaches that lack size-insensitive generalization capacities are unacceptable since an additional training period is repeated for each new instance size. We observe the main obstacle preventing us from training a generalized combinatorial solver is oscillating reward signals. Reward oscillation mainly includes two sides: 1) The conventional reward fails to depict the actual performance of solvers for different instance sizes. 2) The inherent difficulties varying across different sizes worsen training stability. Thus, we present Reward Adjustment Policy Optimization (RAPO), an end-to-end approach to building combinatorial solvers for a wide range of CO problems. RAPO contains a reward adjustment method across instances with variable sizes to address the first side of reward oscillation, along with a promising curriculum strategy to alleviate another side. We conduct experiments on three popular CO problems, namely, the traveling salesman problem (TSP), the capacitated vehicle routing problem (CVRP), and the 0-1 knapsack problem (KP). RAPO exhibits significant improvement in generalization to instances with variable sizes consistently on all benchmarks. Remarkably, RAPO even outperforms its fixed-size counterparts in its well-trained size by a clear margin. 