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# ReZero: Enhancing LLM search ability by trying one-more-time

<img width="2128" alt="ReZeroer" src="https://github.com/user-attachments/assets/f1145fe2-a452-4f23-ab0e-f1518c16aa55" />

ReZero trains a small language model to develop effective search behaviors instead of memorizing static data. It interacts with multiple synthetic search engines, each with unique retrieval mechanisms, to refine queries and persist in searching until it finds exact answers. The project focuses on reinforcement learning, preventing overfitting, and optimizing for efficiency in real-world search applications.

[**Quick Demo**](#quick-demo-) | [**Setup**](#setup-️) | [**Data and Training**](#data-and-training-) | [**Models**](#models-) | [**References**](#references-) | [**Acknowledgements**](#acknowledgements-)

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## Quick Demo 🚀

Run the interactive web interface to see ReZero in action:

```bash
python app.py
```

This will launch a Gradio interface where you can interact with the model and test different search behaviors.

## Setup 🛠️

Clone and install:

```bash
git clone https://github.com/menloresearch/ReZero
cd ReZero
pip install -e .
```

## Data and Training 🧠

All necessary training data is included in the `data/` folder. To train:

```bash
python train_grpo.py
```

## Models 🤖

You can find our models on Hugging Face 🤗! We're committed to open-source and easy access for the research community.

| Model | Backbone | Size | Link |
|-------|----------|------|------|
| ReZero-v0.1 | Llama-3.2-3B | 3B | [🤗 Menlo/ReZero-v0.1-llama-3.2-3b-it-grpo-250404](https://huggingface.co/Menlo/ReZero-v0.1-llama-3.2-3b-it-grpo-250404) |

## References 📖

## Acknowledgements 🤝

- This project is kickstarted from the source code of [AutoDidact](https://github.com/dCaples/AutoDidact)