Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning ...

We investigate Reinforcement Learning (RL) on data without explicit labels for reasoning tasks in Large Language Models (LLMs). The core challenge of the problem is reward estimation during inference ...

Barto and Richard S. Sutton took on the topic in the late 1970s. Eventually, their research led to the creation of reinforcement learning algorithms that sought not to recognize patterns but maximize ...

A technology for hydrogen (H 2) production has been developed by a team of researchers led by Professors Seungho Cho and Kwanyong Seo from the School of Energy and Chemical Engineering at UNIST, in ...

In the meantime, Joby saw an opportunity for potential material savings by replacing the woven reinforcement with a braided reinforcement from A&P Technology. According to Chantel Camardese, product ...

Reinforcement learning (RL) is a powerful technique for enhancing the reasoning capabilities of LLMs, enabling them to develop and refine long Chain-of-Thought (CoT). Models like OpenAI o1 and ...

Abstract: This paper introduces a full solution for decentralized routing in Low Earth Orbit Satellite Constellations (LSatCs) based on continual Deep Reinforcement Learning (DRL), specifically ...

Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the ...