# RULER

## Automated reward function for reinforcement fine-tuning

Relative Universal LLM-Elicited Rewards (RULER) is a general-purpose reward function for reinforcement learning (RL) that uses an LLM-as-judge to rank multiple agent trajectories. It requires no labeled data, expert feedback, or handcrafted reward functions, yet reliably improves agent performance—matching or exceeding handcrafted rewards on 3 of 4 benchmarks. Define your task in the system prompt and RULER handles the rest. RULER is part of the Agent Reinforcement Trainer (ART) open-source framework and is now available on W&B Training Serverless RL.

[Get started](https://art.openpipe.ai/getting-started/quick-start)

## Why RULER?

2-3x faster development  
Skip reward function engineering entirely. Reduce implementation time by 2-3x compared to hand-crafted rewards.

General-purpose  
Works across any task without modification. Apply it to a wide range of RL tasks with a single line of code.

No labeled data required​  
RULER compares trajectories against each other with an LLM-as-judge. No manual labeling or synthetic data needed.

```python

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## Getting started

Simply pip install openpipe-art and [try our sample notebook example](https://art.openpipe.ai/getting-started/quick-start) on W&B Training Serverless RL. It takes just one line of code to use RULER. You can also head over to the [RULER documentation](https://art.openpipe.ai/fundamentals/ruler) to learn more.

## Get started with Serverless RL

[Get started](https://art.openpipe.ai/getting-started/quick-start)