iARCS 用强化学习让 3D 场景生成器听懂自然语言要求，走路、够东西这些约束都能满足，还能提升基础生成器。
iARCS 框架用迭代智能体强化学习，让预训练场景生成器适配自然语言任务要求。它采用两阶段策略：先用通用奖励预训练提升物理合理性，再用 LLM 生成的奖励程序做任务特定微调。实验在可步行性、可达性和间隙约束任务上提升了约束保真度，同时场景多样性保持竞争力。iARCS 生成的数据还能反过来改进基础生成器。
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints. This mismatch limits the usefulness of synthetic data for downstream training, where accessibility, traversability, and spatial rule compliance are often essential. We present iARCS, an iterative agentic reinforcement learning framework that adapts a pretrained scene generator to natural-language task requirements. iARCS uses a two-stage strategy: universal-reward pretraining to improve physical plausibility and layout quality, followed by task-specific fine-tuning with LLM-generated reward programs that are iteratively refined from training feedback. Experiments show improved constraint fidelity on walkability, reachability, and clearance-focused tasks, effective task-specific constraint optimization, and competitive scene diversity. We further show that data generated by iARCS improves a base generator, supporting its value as a practical synthetic data generation tool rather than only a controllable scene editing method.