Research team from Nanjing University proposed FOCUS, a causal model-based offline RL algorithm, which uses causal structure ...

Research team introduced clustered reinforcement learning (CRL), a novel RL framework for efficient exploration in large state spaces or sparse ...

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 ...

In training both models, the company said it used “reinforcement learning”, a technique previously used by other AI companies, including the Chinese startup DeepSeek. OpenAI has also claimed that, ...

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.

Reinforcement Learning does NOT make the base model more intelligent and limits the world of the base model in exchange for early pass performances. Graphs show that after pass 1000 the reasoning ...

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 ...