PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models
arXiv:2605.20873v1 Announce Type: cross 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. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable generation targets. This limits scenario coverage, ties difficulty to surface-level proxies rather than structural sources, and offers limited support for scalable generation, automatic verification
The rapid advancement and widespread deployment of large language models are exposing limitations in current evaluation and training methodologies for complex reasoning tasks.
This development addresses a critical need for robust, verifiable planning capabilities in AI, which is essential for developing performant and reliable autonomous systems.
The ability to generate scalable and verifiable planning data shifts how LLMs will be trained and evaluated for complex tasks, moving beyond fixed datasets to dynamic, controllable scenarios.
- · AI researchers and developers
- · Companies building agentic AI systems
- · Sectors requiring complex task automation
- · Developers relying solely on static, limited benchmarks
- · AI models with weak planning architectures
Improved performance and reliability of LLMs in planning and complex problem-solving.
Acceleration in the development and deployment of sophisticated AI agents across various industries.
Enhanced trust and adoption of AI systems capable of executing multi-step, verifiable plans autonomously.
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Read at arXiv cs.LG