One potential limitation of the hierarchical sequence/set model approach is the scalability to large-scale MILP problems with a high-dimensional action space. As the number of candidate cuts and the complexity of the MILP instances increase, the exploration of the action space becomes more challenging, leading to slower convergence and suboptimal policies. To address this limitation, future research could explore advanced exploration strategies such as hierarchical reinforcement learning with intrinsic motivation, ensemble methods for policy learning, or curriculum learning to gradually increase the complexity of the tasks.
Another limitation could be the generalization of learned policies to unseen MILP instances or problem domains. The hierarchical sequence/set model may struggle to adapt to new problem structures or distributions of candidate cuts, leading to poor performance on unfamiliar instances. To mitigate this limitation, transfer learning techniques, domain adaptation methods, or meta-learning approaches could be investigated to improve the generalization capabilities of the learned policies across diverse MILP scenarios.