Title: Plan2Evolve: LLM Self-Evolution for Improved Planning Capability via Automated Domain Generation
Primary area: applications to robotics, autonomy, planning
Keywords: Robot Task Planning, Task and Motion Planning, LLMs for Planning, Planning Domain Inference, LLM Fine-Tuning, Chain of Thought

Abstract: Large Language Models (LLMs) have recently shown strong potential in robotic task planning, particularly through automatic planning domain generation that integrates symbolic search. Prior approaches, however, have largely treated these domains as search utilities, with limited attention to their potential as scalable sources of reasoning data. At the same time, progress in reasoning LLMs has been driven by chain-of-thought (CoT) supervision, whose application in robotics remains dependent on costly, human-curated datasets. 
We propose Plan2Evolve, an LLM self-evolving framework in which the base model generates planning domains that serve as engines for producing symbolic problem–plan pairs as reasoning traces. These pairs are then transformed into extended CoT trajectories by the same model through natural-language explanations, thereby explicitly aligning symbolic planning structures with natural language reasoning. The resulting data extend beyond the model’s intrinsic planning capacity, enabling model fine-tuning that yields a planning-enhanced LLM with improved planning success, stronger cross-task generalization, and reduced inference costs.

TL;DR: Plan2Evolve utilize planning domain inference method to generate robotic task planning data beyond a model’s own ability, enabling fine-tuning that improves its planning performance.