A reinforcement learning environment is a fail-safe digital practice room where an agent can afford to make mistakes and ...

When RL is paired with human oversight, teams can shape how systems learn, correct course when context changes, and ensure ...

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

For the first time in history, language evolution is partly being steered by machines trained on digital data.

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

Choose the appropriate .yml file for your system. These Anaconda environments use MuJoCo 1.5 and gym 0.10.5. You'll need to get your own MuJoCo key if you want to use MuJoCo. (Optional) If you plan on ...

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 ...

Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Researchers at the University of Science and Technology of China have developed a new reinforcement learning (RL) framework that helps train large language models (LLMs) for complex agentic tasks ...

Landlords could no longer rely on rent-pricing software to quietly track each other's moves and push rents higher using confidential data, under a settlement between RealPage Inc. and federal ...