By dynamically freezing low-rank singular components of diffusion policy weights during training, DRIFT-DAgger cuts training time by roughly 11 to 18 percent while keeping task success near full-rank baselines.
Vision-based trajectory planning via imitation learning for autonomous vehicles
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Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training
By dynamically freezing low-rank singular components of diffusion policy weights during training, DRIFT-DAgger cuts training time by roughly 11 to 18 percent while keeping task success near full-rank baselines.