DyRAD is a method for synthesizing novel radar views in dynamic driving scenes by modeling static background reflectors and motion-tracked dynamic point reflectors to generate complete range-azimuth-Doppler (RAD) tensors. It uses a fixed analytic point-spread function (PSF) to prevent sensor-induced spread from being embedded in the scene representation, enabling zero-shot sensor-configuration transfer.
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DyRAD improves radar scene reconstruction and enables flexible sensor configuration adaptation without retraining.
The paper investigates the scaling properties of on-policy distillation (OPD) in reinforcement learning, focusing on how capabilities transfer between different model scales. It identifies a useful-transfer regime where held-out accuracy increases linearly with the reverse KL divergence from the student's initialization, and finds that smaller teachers can outperform larger ones in capability transfer.
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