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cs.RO2025
RoboSSM: Scalable In-context Imitation Learning via State-Space Models
Youngju Yoo, Jiaheng Hu, Yifeng Zhu +4
In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at dep…
cs.LG2025
Improving Rectified Flow with Boundary Conditions
Xixi Hu, Runlong Liao, Keyang Xu +5
Rectified Flow offers a simple and effective approach to high-quality generative modeling by learning a velocity field. However, we identify a limitation in directly modeling the v…
cs.LG2025
Improving Adaptive Moment Optimization via Preconditioner Diagonalization
Son Nguyen, Bo Liu, Lizhang Chen +1
Modern adaptive optimization methods, such as Adam and its variants, have emerged as the most widely used tools in deep learning over recent years. These algorithms offer automatic…