In February 2026, Tencent tore down its pre-training and reinforcement-learning infrastructure and rebuilt both from scratch.
Discovering and characterizing new materials is important for unlocking advances in fields like clean energy, advanced ...
Discover how combining feedback and reflection helps students improve skills, challenge assumptions, and become more adaptive ...
If it feels like AI is developing too fast to keep up with, a group of Chinese researchers have some bad news – because ...
AI is reshaping how India's students learn, offering instant, personalized help that challenges edtech’s paid models and ...
A new agentic AI application aims to speed up drug development, helping bring new medical treatments to patients faster.
There is an exciting future on the horizon—one in which your thoughts could directly control electronic devices you use every ...
In this tutorial, we implement a Colab-ready version of the AutoResearch framework originally proposed by Andrej Karpathy. We build an automated experimentation pipeline that clones the AutoResearch ...
Abstract: As a closed-loop learning control method, repetitive control has been widely used in a variety of areas from appliances to aviation. A repetitive control system features perfect reference ...
Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.
Abstract: This paper develops learning-enabled safe controllers for linear systems subject to system uncertainties and bounded disturbances. Given the disturbance zonotope, the data based closed-loop ...
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