Travel planning is a sophisticated decision-making process that requires synthesizing multifaceted information to construct itineraries. However, existing travel planning approaches face several challenges: (1) Pruning candidate points of interest (POIs) while maintaining a high recall rate; (2) A single reasoning path restricts the exploration capability within the feasible solution space for travel planning; (3) Simultaneously optimizing hard constraints and soft constraints remains a significant difficulty. To address these challenges, we propose TourPlanner, a comprehensive framework featuring multi-path reasoning and constraint-gated reinforcement learning. Specifically, we first introduce a Personalized Recall and Spatial Optimization (PReSO) workflow to construct spatially-aware candidate POIs' set. Subsequently, we propose Competitive consensus Chain-of-Thought (CCoT), a multi-path reasoning paradigm that improves the ability of exploring the feasible solution space. To further refine the plan, we integrate a sigmoid-based gating mechanism into the reinforcement learning stage, which dynamically prioritizes soft-constraint satisfaction only after hard constraints are met. Experimental results on travel planning benchmarks demonstrate that TourPlanner achieves state-of-the-art performance, significantly surpassing existing methods in both feasibility and user-preference alignment.

翻译：旅行规划是一个复杂的决策过程，需要综合多方面的信息来构建行程。然而，现有的旅行规划方法面临若干挑战：（1）在保持高召回率的同时对候选兴趣点（POI）进行剪枝；（2）单一推理路径限制了旅行规划在可行解空间内的探索能力；（3）同时优化硬约束和软约束仍然是一个重大难题。为应对这些挑战，我们提出了TourPlanner，一个具备多路径推理和约束门控强化学习的综合性框架。具体而言，我们首先引入个性化召回与空间优化（PReSO）工作流来构建具有空间感知的候选POI集合。随后，我们提出竞争性共识思维链（CCoT），这是一种多路径推理范式，旨在提升对可行解空间的探索能力。为了进一步优化规划方案，我们在强化学习阶段集成了一个基于Sigmoid函数的门控机制，该机制仅在满足硬约束后，才动态地优先考虑软约束的满足。在旅行规划基准测试上的实验结果表明，TourPlanner实现了最先进的性能，在可行性和用户偏好契合度方面均显著超越了现有方法。