Practical overview
When it fits
Use it when agent training requires repeated environment interaction and explicit control over step-level inputs, histories or memory. Its design is especially relevant to long-horizon, multi-turn research workflows.
Who it is for
- Researchers training LLM agents with reinforcement learning.
- Engineers building self-hosted multi-turn agent-training workflows.
- Teams experimenting with long-horizon language or vision-language policies.
- Developers comparing reinforcement-learning algorithms and memory designs.
Documented uses
- Train multi-turn LLM agents through agent-environment interaction.
- Train vision-language agents on text-and-image tasks.
- Customize rollout histories, per-step inputs and memory modules.
- Run policy-optimization experiments across supported environments and algorithms.
Requirements & limits
The repository is public and lists an Apache-2.0 license. The technical material describes Linux, command-line and self-hosted use.
- Whether the required workflow is training infrastructure rather than a ready-to-use agent application.
- Need for step-independent multi-turn rollouts and customizable memory or history handling.
- Choice of supported environment, including the experimental AppWorld entry.
- Need for text-only or multimodal model training, with listed support for Qwen, Qwen-VL and LLaMA3.2 families.
- Whether the selected experiment can use one of the listed reinforcement-learning algorithms or LoRA training.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Verl Agent and its documented environments
Prepare the veRL-based framework and install a supported agent environment before following a repository example.
Before you start
- A Linux, command-line, self-hosted Python environment.
- The Verl Agent repository.
- veRL and any selected supported environment from the repository installation sections.
Steps
Obtain the Verl Agent repository from GitHub.
Install veRL as directed in the repository's installation section.
Install one of the listed environments, such as ALFWorld, WebShop, Search, Sokoban, Gym Cards or AppWorld.
Continue to the corresponding repository example for the selected training method or agent workflow.
Expected outcome
A locally prepared Verl Agent and veRL environment for proceeding to the repository's examples.
Guide limitations
- The supplied material does not specify hardware requirements, compatibility details for external memory systems or a completed training result.
- AppWorld is identified as experimental in the installation contents.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- langfengQ/verl-agent · Official homepage title · retained in its source language