EnergyFlow shows that denoising score matching on diffusion policies recovers the gradient of the expert's soft Q-function under maximum-entropy optimality, enabling non-adversarial reward extraction and improved policy generalization.
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A survey of VLA robotics research identifies data infrastructure as the primary bottleneck and distills four open challenges in representation alignment, multimodal supervision, reasoning assessment, and scalable data generation.
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Recovering Hidden Reward in Diffusion-Based Policies
EnergyFlow shows that denoising score matching on diffusion policies recovers the gradient of the expert's soft Q-function under maximum-entropy optimality, enabling non-adversarial reward extraction and improved policy generalization.
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Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines
A survey of VLA robotics research identifies data infrastructure as the primary bottleneck and distills four open challenges in representation alignment, multimodal supervision, reasoning assessment, and scalable data generation.