LLMs Enhance Spatial Reasoning with Building Blocks and Planning
Published:Dec 31, 2025 00:36
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of spatial reasoning in LLMs, a crucial capability for applications like navigation and planning. The authors propose a novel two-stage approach that decomposes spatial reasoning into fundamental building blocks and their composition. This method, leveraging supervised fine-tuning and reinforcement learning, demonstrates improved performance over baseline models in puzzle-based environments. The use of a synthesized ASCII-art dataset and environment is also noteworthy.
Key Takeaways
- •Proposes a two-stage approach for spatial reasoning in LLMs.
- •Uses supervised fine-tuning for elementary spatial transformations.
- •Employs reinforcement learning with LoRA adapters for multi-step planning.
- •Outperforms baselines in puzzle-based environments.
- •Utilizes a synthesized ASCII-art dataset and environment.
Reference
“The two-stage approach decomposes spatial reasoning into atomic building blocks and their composition.”