verl-agent and verl-tool
These are ecosystem siblings where verl-agent builds upon verl's core reinforcement learning infrastructure to add agent-specific training capabilities, making them designed to be used together rather than as alternatives.
About verl-agent
langfengQ/verl-agent
verl-agent is an extension of veRL, designed for training LLM/VLM agents via RL. verl-agent is also the official code for paper "Group-in-Group Policy Optimization for LLM Agent Training"
This tool helps AI researchers and machine learning engineers train large language models (LLMs) and vision-language models (VLMs) to act as intelligent agents in complex, multi-step environments. You input an LLM/VLM and a task environment (like ALFWorld or WebShop), and it outputs a fine-tuned agent capable of performing long-horizon tasks through reinforcement learning. It's designed for researchers developing advanced AI agents that interact dynamically and remember key information over many turns.
About verl-tool
TIGER-AI-Lab/verl-tool
A version of verl to support diverse tool use
This project helps AI developers build, train, and evaluate intelligent agents that can effectively use external tools to complete tasks. It takes your agent's code and definitions of available tools, then provides a robust framework to train the agent to select and interact with these tools. The output is a well-trained agent capable of complex, multi-step problem-solving using its integrated tools. This is for AI researchers and engineers developing sophisticated tool-using AI models.
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