It has been a popular trend in AI to pretrain foundation models on massive data. However, collecting sufficient offline training trajectories for robot learning is particularly expensive since valid control actions are required. Therefore, most existing robotic datasets are collected from human experts. We tackle such a data collection issue with a new framework called "robot self-teaching", which asks the robot to self-generate effective training data instead of relying on human demonstrators. Our key idea is to train a separate data-generation policy operating on the state space to automatically generate meaningful actions and trajectories with ever-growing complexities. Then, these generated data can be further used to train a visual policy with strong compositional generalization capabilities. We validate our framework in two visual manipulation testbeds, including a multi-object stacking domain and a popular RL benchmark "Franka kitchen". Experiments show that the final visual policy trained on self-generated data can accomplish novel testing goals that require long-horizon robot executions. Project website https://sites.google.com/view/robot-self-teaching.

Quantization of large language models (LLMs) faces significant challenges, particularly due to the presence of outlier activations that impede efficient low-bit representation. Traditional approaches predominantly address Normal Outliers, which are activations across all tokens with relatively large magnitudes. However, these methods struggle with smoothing Massive Outliers that display significantly larger values, which leads to significant performance degradation in low-bit quantization. In this paper, we introduce DuQuant, a novel approach that utilizes rotation and permutation transformations to more effectively mitigate both massive and normal outliers. First, DuQuant starts by constructing the rotation matrix, using specific outlier dimensions as prior knowledge, to redistribute outliers to adjacent channels by block-wise rotation. Second, We further employ a zigzag permutation to balance the distribution of outliers across blocks, thereby reducing block-wise variance. A subsequent rotation further smooths the activation landscape, enhancing model performance. DuQuant simplifies the quantization process and excels in managing outliers, outperforming the state-of-the-art baselines across various sizes and types of LLMs on multiple tasks, even with 4-bit weight-activation quantization. Our code is available at https://github.com/Hsu1023/DuQuant.