Explore how DeepSeek R1 combines reinforcement learning, GRPO, and supervised fine-tuning into a cutting-edge LLM.

Nature is brimming with animals that collaborate in large numbers. Bees stake out the best feeding spots and let others know where they are. Ants construct complex hierarchical homes built for defense ...

Beyond high performance, the RL framework’s main advantage lies in its real-time application potential. Once trained, the ...

In this modern era, Reinforcement Learning (RL) has evolved from theoretical research to a transformative force driving significant changes in industrial applications. Debu Sinha, a recognized ...

The Association for Computing Machinery, today announced the recipients of three prestigious technical awards. This year’s ...

A PyTorch-based implementation of Adversarial Inverse Reinforcement Learning (AIRL) for vision-based continuous-control drone navigation. This repository provides training, evaluation, and reward ...

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 ...

We forget up to 90 percent of what we learn within a week. But it doesn’t have to be that way. You can beat the forgetting curve and make your learning stick—for good.

In reinforcement learning, the feedback you get is either ... and the temporal difference algorithm was designed to deal with it. It’s based on animal learning theory, where predictors of reward act ...

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 ...

This challenge is modeled as a non-cooperative game, and the existence of a Nash Equilibrium is demonstrated using potential game theory. To solve the game, Best Response Dynamics and a log-linear ...