Planner-R1: Reward Shaping Enables Efficient Agentic RL with Smaller LLMs

Siyu Zhu, Yanbin Jiang, Hejian Sang +6

We investigated Agentic RL with large language models on the \textsc{TravelPlanner} benchmark. Our approach, \textsc{Planner-R1}, achieved a \textbf{56.9\%} final-pass rate with on…