Agent2World: Generating Symbolic World Models with Multi-Agent Feedback
Published:Dec 26, 2025 18:54
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of training LLMs to generate symbolic world models, crucial for model-based planning. The lack of large-scale verifiable supervision is a key limitation. Agent2World tackles this by introducing a multi-agent framework that leverages web search, model development, and adaptive testing to generate and refine world models. The use of multi-agent feedback for both inference and fine-tuning is a significant contribution, leading to improved performance and a data engine for supervised learning. The paper's focus on behavior-aware validation and iterative improvement is a notable advancement.
Key Takeaways
- •Agent2World is a multi-agent framework for generating symbolic world models.
- •It uses web search, model development, and adaptive testing.
- •The framework provides feedback for both inference and fine-tuning.
- •It achieves state-of-the-art results on multiple benchmarks.
- •Fine-tuning on trajectories generated by the testing team significantly improves performance.
Reference
“Agent2World demonstrates superior inference-time performance across three benchmarks spanning both Planning Domain Definition Language (PDDL) and executable code representations, achieving consistent state-of-the-art results.”