SCRIPT presents a scalable diffusion policy with JAST-DiT architecture, nonlinear history conditioning, and RLHR post-training that claims to outperform prior methods on text alignment, motion quality, and physical realism while scaling on a 1200-hour dataset.
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TRIRL uses a trust-region insight to allow explicit dual ascent in IRL with local policy searches, claiming monotonic improvement and better generalization than prior methods.
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SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control
SCRIPT presents a scalable diffusion policy with JAST-DiT architecture, nonlinear history conditioning, and RLHR post-training that claims to outperform prior methods on text alignment, motion quality, and physical realism while scaling on a 1200-hour dataset.
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Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
TRIRL uses a trust-region insight to allow explicit dual ascent in IRL with local policy searches, claiming monotonic improvement and better generalization than prior methods.