A new deep reinforcement learning framework improves personalized e-commerce marketing by balancing short-term conversions ...

Hierarchical world-model planning raised pick-and-place success from zero to seventy percent on a real Franka robot without ...

Researchers at Lorestan University have combined ant colony optimization, reinforcement learning, and opposition-based ...

This is a report on "HYSET," a new technology that solves the fundamental problem faced by current LLM agents—task failure ...

Learning to drive a manual car requires understanding the clutch, accelerator, brake, and gear lever. Key skills include ...

Introduction A few years ago, I was tasked with creating a demand forecasting prototype on the side of my regular work. I ...

Abstract: Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is introduced ...

The National Security Agency, the Cybersecurity and Infrastructure Security Agency, and the Federal Bureau of Investigation released a joint cybersecurity advisory on September 8, 2026, warning that ...

In this tutorial, we walk through a complete, hands-on journey of post-training large language models using the powerful TRL (Transformer Reinforcement Learning) library ecosystem. We start from a ...

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning ...