PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models
PlanningBench is a new framework designed to generate scalable and verifiable planning data for evaluating and training large language models. It addresses limitations in existing benchmarks by allowing for controllable generation of planning scenarios based on a structured taxonomy. The framework has shown to improve performance in planning tasks and instruction-following tasks through reinforcement learning on verified data.
- ▪PlanningBench abstracts practical workflows into a structured taxonomy of over 30 task types and difficulty factors.
- ▪The framework enables adaptive difficulty control and quality filtering for planning problems.
- ▪Current large language models struggle to produce complete solutions under coupled constraints, as revealed by evaluations using PlanningBench.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20873 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
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Computer Science > Artificial Intelligence arXiv:2605.20873 (cs) [Submitted on 20 May 2026] Title:PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models Authors:Ziliang Zhao, Zenan Xu, Shuting Wang, Hongjin Qian, Yan Lei, Minda Hu, Zhao Wang, Shihan Dou, Zhicheng Dou, Pluto Zhou View a PDF of the paper titled PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models, by Ziliang Zhao and 9 other authors View PDF Abstract:Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequences into executable and verifiable solutions.
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