Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says ...

Leaders, whether in boardrooms or garages, constantly face an unchanging force: uncertainty. For a CEO, making a good decision always involves factoring in as much data as possible, and then trusting ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Abstract: In the digital realm, ensuring the security and reliability of systems and software is of paramount importance. Fuzzing has emerged as one of the most effective testing techniques for ...

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.

In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO ...

This study introduces a novel multiagent reinforcement learning (MARL) algorithm designed for identifying and optimizing personalized recommendations in bipolar disorder. The algorithm leverages ...

The domain of LLMs has rapidly evolved to include tools that empower these models to integrate external knowledge into their reasoning processes. A significant advancement in this direction is ...

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...

In recent years, Large Language Models (LLMs) have significantly redefined the field of artificial intelligence (AI), enabling machines to understand and generate human-like text with remarkable ...