#policy-training News & Analysis

3 articles tagged with #policy-training. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · May 117/10

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Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training

Researchers introduce Sword, a world model framework that improves Vision-Language-Action (VLA) models' ability to simulate environments for policy training. By addressing visual style sensitivity and error accumulation in long-horizon predictions, Sword demonstrates significant performance gains on the LIBERO benchmark, advancing the feasibility of training AI agents entirely within simulated environments.

AINeutralarXiv – CS AI · Jun 256/10

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ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles

ReaDy-Go introduces a real-to-sim simulation pipeline using 3D Gaussian Splatting to generate photorealistic dynamic environments with moving obstacles for training robust visual navigation policies. The system synthesizes realistic human avatars and motions within reconstructed scenes, enabling policies to better transfer from simulation to real-world deployment across various environments.

AINeutralarXiv – CS AI · Jun 196/10

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Temporal Self-Imitation Learning

Researchers introduce Temporal Self-Imitation Learning (TSIL), a reinforcement learning framework that improves robot manipulation training by identifying and reusing efficient successful trajectories as self-supervision signals. The approach outperforms traditional reward-shaping methods across 15 long-horizon tasks by leveraging temporal efficiency as an intrinsic learning signal rather than relying solely on manually engineered rewards.