3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience

Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural language remains a major challenge. In this work, we introduce 3DarwAgent, a training-free, language-driven framework for 3D sketch generation that leverages large language models (LLMs) to sequentially draw 3D Bézier curves under geometric feedback. Unlike prior 2D sketch agents, our method introduces a relative experience optimization strategy that tailors the recently proposed Generalized Reward Policy Optimization (GRPO) paradigm. Instead of relying on explicit ground-truth supervision, we construct pairwise comparisons among generated sketches, i.e., each pair consisting of a relatively better and worse result based on CLIP-based perceptual rewards and LLM-based fine-grained qualitative assessment. These experiences are then used to iteratively refine the prior knowledge of 3D drawing, enabling black-box reinforcement of the model’s 3D awareness. This design allows our model to self-improve its spatial understanding and drawing quality without parameter updates. Experiments show that 3DarwAgent can generate complex and coherent 3D Bézier sketches from textual prompts, exhibit emergent geometric reasoning, and generalize to novel shapes, establishing a new paradigm for training-free 3D sketch intelligence.