Despite notable advancements in large language models (LLMs), effective performance on reasoning-intensive tasks—such as mathematical problem solving ... and minimal but well-targeted reinforcement ...

Computing pioneer Alan Turing suggested training machines with rewards and punishments. Two computer scientists put the idea into practice in the 1980s and set the stage for the likes of ChatGPT.

Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.

Explore the hidden trade-offs of reinforcement learning in AI and why base models might hold the key to true intelligence.

A team of AI researchers at the University of California, Los Angeles, working with a colleague from Meta AI, has introduced d1, a diffusion-large-language-model-based framework that has been improved ...

Let’s move on to temporal difference learning (TD learning), which is a subset of reinforcement learning that was the focus ...

Researchers from Stanford University and Google DeepMind have unveiled Step-Wise Reinforcement Learning (SWiRL), a technique designed to enhance the ability of large language models (LLMs) to tackle ...