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 gen
Record details
Published: 6 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 7 August 2026
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ethics.ai (6 August 2026), “iARCS: Iterative Agentic RL for Controllable 3D Scene Generation,” evidence record 17342, https://ethics.ai/record/17342 (originally published by arXiv cs.AI).
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