Reinforcement Learning Framework for AI Agents
5.2k 2026-04-30
Gen-Verse/OpenClaw-RL
An asynchronous reinforcement learning framework enabling personalized AI agent training through natural language conversations and scalable real-world deployments.
Core Features
Train personalized AI agents using natural language feedback.
Scalable RL for real-world scenarios: terminal, GUI, SWE, and tool-call settings.
Fully asynchronous architecture with options for zero API or zero GPU usage.
Supports various large language models (e.g., Qwen) and LoRA training.
Features hybrid RL methods and automatic optimization.
Detailed Introduction
OpenClaw-RL is a groundbreaking asynchronous reinforcement learning framework designed to transform everyday conversations into powerful training signals for personalized AI agents. Unlike traditional RL-for-LLM systems that rely on centralized, batch-mode training with pre-collected datasets, OpenClaw-RL adopts a fundamentally different approach, enabling scalable RL implementations for general agent settings across terminal, GUI, SWE, and tool-call scenarios, focusing on real-world applicability and efficiency.