constraint-driven

PlanningBench is a framework for generating scalable, diverse, and verifiable planning data to evaluate and train large language models, featuring a constraint-driven synthesis pipeline with adaptive difficulty control and quality filtering. Experiments show that frontier LLMs struggle with coupled constraints, and reinforcement learning on PlanningBench data improves performance on unseen planning tasks.