agentskillexchange 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.
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