Train Agent Policies With Rllm Reinforcement Learning

---
name: "Train agent policies with rLLM reinforcement learning"
slug: "train-agent-policies-with-rllm-reinforcement-learning"
description: "Use rLLM to evaluate, trace, reward, and train LLM agents with reinforcement learning across common agent frameworks."
github_stars: 5530
verification: "security_reviewed"
source: "https://github.com/rllm-org/rllm"
author: "rLLM"
publisher_type: "organization"
category: "Developer Tools"
framework: "Multi-Framework"
tool_ecosystem:
  github_repo: "rllm-org/rllm"
  github_stars: 5530
---

# Train agent policies with rLLM reinforcement learning

Use rLLM to evaluate, trace, reward, and train LLM agents with reinforcement learning across common agent frameworks.

## Prerequisites

Python 3.11 or newer, rLLM, agent code or benchmark task, reward/evaluator function, optional Tinker or verl training backend

## Installation

Use the upstream install or setup path that matches your environment:
- uv pip install "rllm @ git+https://github.com/rllm-org/rllm.git"
- uv pip install rllm[verl] @ git+https://github.com/rllm-org/rllm.git

Requirements and caveats from upstream:
- rLLM requires Python >= 3.11. You can install it either directly via pip or build from source.
- For building from source or Docker, see the [installation guide](https://docs.rllm-project.com/installation).
- ### Option B: Python API

Basic usage or getting-started notes:
- bash
- this installs dependencies for running rllm cli, which uses Tinker as the training backend.
- To use verl as the training backend (GPU machine required), install via

- Source: https://github.com/rllm-org/rllm
- Extracted from upstream docs: https://raw.githubusercontent.com/rllm-org/rllm/HEAD/README.md

## Documentation

- https://docs.rllm-project.com

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/train-agent-policies-with-rllm-reinforcement-learning/)