iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

TL;DR - iARCS is an iterative agentic RL framework that adapts a pretrained 3D scene generator to natural-language task requirements, so synthetic scenes satisfy functional constraints (walkability, reachability, clearance) rather than just looking realistic. It matters because constraint-faithful synthetic scenes are more useful as training data for computer vision and embodied AI.

• Two-stage training: universal-reward pretraining for physical plausibility and layout quality, then task-specific fine-tuning against the stated natural-language requirements.
• Reward functions are LLM-generated reward programs that are iteratively refined using feedback from training, avoiding hand-crafted per-task reward engineering.
• Reported gains in constraint fidelity on walkability, reachability, and clearance tasks, with competitive scene diversity retained.
• Data produced by iARCS improves the base generator, positioning it as a synthetic data generation pipeline rather than only a controllable scene editing tool.