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Documentation Index

Fetch the complete documentation index at: https://docs.rllm-project.com/llms.txt

Use this file to discover all available pages before exploring further.

This guide gets you from zero to running an evaluation and launching RL training using only the rllm CLI — no Python scripts required.
rLLM CLI showing the banner, available commands including agent, dataset, eval, init, login, model, and train, along with global options

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Prerequisites

  • rLLM installed (see installation)
  • An API key for a model provider (OpenAI, Anthropic, Together, etc.)

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Step 1: Configure your model

Run the interactive setup to select a provider and model: 
rllm model setup
You’ll be prompted to:
  1. Choose a provider (e.g., OpenAI)
  2. Enter your API key
  3. Pick a default model (e.g., gpt-4o)
Your configuration is saved to ~/.rllm/config.json. You can switch providers later with rllm model swap.

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Step 2: Explore available datasets

Browse the full catalog of 50+ benchmarks: 
rllm dataset list --all
To preview what a dataset looks like: 
rllm dataset inspect gsm8k -n 3

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Step 3: Run an evaluation

Evaluate your model on a benchmark: 
rllm eval gsm8k
That’s it. rLLM will:
  1. Auto-pull the dataset from HuggingFace
  2. Start a local LiteLLM proxy for your configured provider
  3. Resolve the default agent and evaluator from the catalog
  4. Run the evaluation with 64 concurrent requests
  5. Print accuracy, error count, and per-signal metrics
For a quick test run, limit the number of examples:
rllm eval gsm8k --max-examples 20

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Evaluate with a local model

If you’re running a model server (vLLM, SGLang, etc.), point to it directly: 
rllm eval gsm8k --base-url http://localhost:30000/v1 --model Qwen/Qwen3-4B

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Step 4: Train with RL

Launch reinforcement learning training on a benchmark: 
rllm train gsm8k --model Qwen/Qwen3-8B
This starts the unified training pipeline with the tinker backend, using GRPO for advantage computation and LoRA for efficient fine-tuning. Customize training hyperparameters: 
rllm train gsm8k \
  --model Qwen/Qwen3-8B \
  --batch-size 16 \
  --group-size 4 \
  --lr 1e-5 \
  --max-steps 50

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Step 5: Build a custom agent

Scaffold a new agent project: 
rllm init my-agent --template react
Install it and use it immediately: 
cd my-agent
pip install -e .
rllm eval gsm8k --agent my-agent

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What’s next

CLI reference

Full reference for all commands and flags

Supported datasets

Browse 50+ benchmarks across math, code, QA, VLM, and more

Unified trainer

Dive into the training pipeline and configuration

SDK overview

Use any LLM framework with SDK-based training