A two-stage agentic RL framework with LLM-generated, iteratively refined reward programs improves functional constraint fidelity in 3D scene generation and enables self-augmentation of the base generator.
Training diffusion models with reinforce- ment learning, 2023
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iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
A two-stage agentic RL framework with LLM-generated, iteratively refined reward programs improves functional constraint fidelity in 3D scene generation and enables self-augmentation of the base generator.