---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
library_name: transformers
pipeline_tag: text-generation
---

# TARS-7B

## Overview

**TARS-7B** is an open-source reasoning model trained for safety using **TARS**: *Training Adaptive Reasoners for Safety* introduced in the paper: [**Reasoning as an Adaptive Defense for Safety**](https://arxiv.org/abs/2507.00971), to facilitate the research of reasoning models for LLM safety. This model is trained using a mixing ratio of \\(\lambda = 0.5\\) between harmful and harmless prompts, starting from the base model [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).

TARS is a simple but effective online reinforcement learning (RL) method that trains models to **adaptively reason** for **low refusal** and **safe behavior**, using three key ingredients:

### 🔑 Key Ingredients
- **Ingredient 1:** Lightweight supervised fine-tuning (SFT) for diverse generations
- **Ingredient 2:** Mixing in harmless prompts during RL training  
- **Ingredient 3:** Decoupled reward model for better exploration  

For full details, please check out our [paper](https://arxiv.org/pdf/2507.00971) or [blogpost](https://training-adaptive-reasoners-safety.github.io).

---

## 📖 Citation

If you use **TARS-7B** in your work, please cite us:

```bibtex
@article{kim2025reasoning,
  title={Reasoning as an Adaptive Defense for Safety},
  author={Kim, Taeyoun and Tajwar, Fahim and Raghunathan, Aditi and Kumar, Aviral},
  journal={arXiv preprint arXiv:2507.00971},
  year={2025}
}