TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning
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.
6965b35dfc8c4ecc02c7f897
Yinuo Wang
6965b35dfc8c4ecc02c7f898
Mining Tan
6965b35dfc8c4ecc02c7f899
Wenxiang Jiao
claimed_verified
2026-02-27T16:46:25.270Z
63db16330cc3bc12bc0b6f8f
https://cdn-avatars.huggingface.co/v1/production/uploads/63db16330cc3bc12bc0b6f8f/ld0JQIfX1SBlDVDOmw9VT.jpeg
Wenxiang Jiao
wxjiao
user
wxjiao
6965b35dfc8c4ecc02c7f89a
Xiaoxi Li
6965b35dfc8c4ecc02c7f89b
Hao Wang
6965b35dfc8c4ecc02c7f89c
Xuanyu Zhang
6965b35dfc8c4ecc02c7f89d
Yuan Lu
6965b35dfc8c4ecc02c7f89e
Weiming Dong
TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning
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.
TourPlanner addresses travel planning challenges through multi-path reasoning and constraint-gated reinforcement learning to optimize both hard and soft constraints effectively.
multi-path reasoning constraint-gated reinforcement learning Personalized Recall and Spatial Optimization CCoT spatially-aware candidate POIs feasible solution space hard constraints soft constraints