Shypd.aiThe largest open index of AI toolsverl-agent is an AI Agents & Automation tool that extends veRL for training LLM/VLM agents via reinforcement learning. It supports multi-turn interactions and customizable memory modules for scalable long-horizon tasks.
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verl-agent is an extension of veRL specifically designed for training large language model (LLM) and vision-language model (VLM) agents using reinforcement learning (RL). It is the official code for the paper "Group-in-Group Policy Optimization for LLM Agent Training." A key differentiator is its step-independent multi-turn rollout mechanism, which allows for fully customizable per-step input structures, flexible history management, and modular memory. This design makes verl-agent highly scalable for very long-horizon, multi-turn RL training, such as tasks requiring up to 50 steps. The framework provides a diverse set of RL algorithms, including GiGPO, GRPO, and PPO, along with a rich suite of agent environments for both visual and text-based tasks, supporting models like Qwen3, LLaMA3.2, and LoRA fine-tuning.
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