Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation

• URL: http://arxiv.org/abs/2507.02253v1
• Date: Thu, 03 Jul 2025 03:02:49 GMT
• Title: Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
• Authors: Jungkoo Kang,
• Abstract summary: NL2FLOW is a fully automated system for parametrically generating planning problems.<n>Highest problem models achieved 86% success in generating valid plans.<n>Highest success rate for translating natural language into a representation of a plan was lower than the highest rate of generating a valid plan directly.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Progress in enhancing large language model (LLM) planning and reasoning capabilities is significantly hampered by the bottleneck of scalable, reliable data generation and evaluation. To overcome this, I introduce NL2FLOW, a fully automated system for parametrically generating planning problems - expressed in natural language, a structured intermediate representation, and formal PDDL - and rigorously evaluating the quality of generated plans. I demonstrate NL2FLOW's capabilities by generating a dataset of 2296 problems in the automated workflow generation domain and evaluating multiple open-sourced, instruct-tuned LLMs. My results reveal that the highest performing models achieved 86% success in generating valid plans and 69% in generating optimal plans, specifically for problems with feasible solutions. Regression analysis shows that the influence of problem characteristics on plan generation is contingent on both model and prompt design. Notably, I observed that the highest success rate for translating natural language into a JSON representation of a plan was lower than the highest rate of generating a valid plan directly. This suggests that unnecessarily decomposing the reasoning task - introducing intermediate translation steps - may actually degrade performance, implying a benefit to models capable of reasoning directly from natural language to action. As I scale LLM reasoning to increasingly complex problems, the bottlenecks and sources of error within these systems will inevitably shift. Therefore, a dynamic understanding of these limitations - and the tools to systematically reveal them - will be crucial for unlocking the full potential of LLMs as intelligent problem solvers.

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