Tags: #reinforcement-learning

A scalable asynchronous reinforcement learning infrastructure designed to bridge foundation model training with modern LLM-based agent applications.

An open-source framework for training multi-step LLM agents using reinforcement learning (GRPO) to improve their reliability and performance on real-world tasks, with an optional serverless training service.

An easy-to-use, scalable, and high-performance open-source framework for Reinforcement Learning from Human Feedback (RLHF), leveraging Ray and vLLM for distributed training of LLMs and VLMs.

A versatile framework designed to train and optimize AI agents from any framework with minimal code changes, leveraging advanced algorithms like Reinforcement Learning.

An efficient and user-friendly scaling library designed to optimize Reinforcement Learning with Large Language Models, enhancing performance in complex AI tasks.

An asynchronous reinforcement learning framework enabling personalized AI agent training through natural language conversations and scalable real-world deployments.

VLM-R1 is a stable and generalizable R1-style Large Vision-Language Model that leverages reinforcement learning to significantly improve visual understanding tasks.

A scalable asynchronous reinforcement learning infrastructure designed to bridge foundation model training with modern LLM-based agent applications.