TARS-1.5B 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, 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 Qwen2.5-1.5B-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 or blogpost.

📖 Citation

If you use TARS-1.5B 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}
}

CMU-AIRe/TARS-1.5B

TARS-1.5B

Overview

🔑 Key Ingredients

📖 Citation

CMU-AIRe/
TARS-1.5B