Abstract: The exponential growth in the number of Internet of Things (IoT) devices and the vast quantity of data they generate present a significant challenge to the efficacy of traditional ...

The new reinforcement learning system lets large language models challenge and improve themselves using real-world data instead of curated training sets. Meta researchers have unveiled a new ...

Inquiry-based learning has been gaining more attention in classrooms across the world. Teachers often ask, What does it look like in practice? How does it differ from more traditional approaches? At ...

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...

From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...

In the context of mass higher education, Chinese application-oriented undergraduate institutions face significant teaching challenges stemming from the increasingly diverse student population. This ...

South Wales Police has emerged as a pioneer in the deployment of facial recognition technology in the United Kingdom, particularly in implementing live facial recognition (LFR) systems. The force has ...

Challenge-based learning is a form of collaborative and project-based learning in which students band together to help solve a local or national problem in a way that furthers their learning.