# Train agent policies with rLLM reinforcement learning

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

- Skill: `agentskillexchange/train-agent-policies-with-rllm-reinforcement-learning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add agentskillexchange/train-agent-policies-with-rllm-reinforcement-learning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/agentskillexchange/train-agent-policies-with-rllm-reinforcement-learning/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: agentskillexchange (https://skillmd.com/u/agentskillexchange)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/agentskillexchange/train-agent-policies-with-rllm-reinforcement-learning

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# 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/)