TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios
Authors: name: Yuanzhe Shen, name: Zisu Huang, name: Zhengyuan Wang, name: Muzhao Tian, name: Zhengkang Guo, name: Chenyang Zhang, name: Shuaiyu Zhou, name: Zengjie Hu, name: Dailin Li, name: Jingwen Xu, name: Kaimin Wang, name: Wenhao Liu, name: Tianlong Li, name: Fengpeng Yue, name: Feng Hong, name: Cao Liu, name: Ke Zeng
Organization: meituan

As LLM-based agents are deployed in increasingly complex real-world settings, existing benchmarks underrepresent key challenges such as enforcing global constraints, coordinating multi-tool reasoning, and adapting to evolving user behavior over long, multi-turn interactions. To bridge this gap, we introduce TRIP-Bench, a long-horizon benchmark grounded in realistic travel-planning scenarios. TRIP-Bench leverages real-world data, offers 18 curated tools and 40+ travel requirements, and supports automated evaluation. It includes splits of varying difficulty; the hard split emphasizes long and ambiguous interactions, style shifts, feasibility changes, and iterative version revision. Dialogues span up to 15 user turns, can involve 150+ tool calls, and may exceed 200k tokens of context. Experiments show that even advanced models achieve at most 50\% success on the easy split, with performance dropping below 10\% on hard subsets. We further propose GTPO, an online multi-turn reinforcement learning method with specialized reward normalization and reward differencing. Applied to Qwen2.5-32B-Instruct, GTPO improves constraint satisfaction and interaction robustness, outperforming Gemini-3-Pro in our evaluation. We expect TRIP-Bench to advance practical long-horizon interactive agents, and GTPO to provide an effective online RL recipe for robust long-horizon training.