iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
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Merged summary
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.
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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.