---
library_name: transformers
pipeline_tag: text-generation
---

# GDSD: Guided Denoiser Self-Distillation for Diffusion Language Models

This repository contains a model checkpoint from the paper [GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models](https://huggingface.co/papers/2605.29398). 

Guided Denoiser Self-Distillation (GDSD) is a reinforcement learning framework that improves the denoiser of diffusion large language models (dLLMs) by distilling from an advantage-guided self-teacher. This approach bypasses the biases of traditional ELBO-based methods and provides more stable training dynamics for dLLMs across planning, math, and coding benchmarks.

## Links

- **Paper**: [https://arxiv.org/abs/2605.29398](https://arxiv.org/abs/2605.29398)
- **Code**: [https://github.com/GaryBall/GDSD](https://github.com/GaryBall/GDSD)

## Citation

If you find GDSD helpful, please consider citing:

```bibtex
@misc{tang2026gdsdreinforcementlearningguided,
      title={GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models}, 
      author={Xiaohang Tang and Keyue Jiang and Che Liu and Qifang Zhao and Xiaoxiao Xu and Sangwoong Yoon and Ilija Bogunovic},
      year={2026},
      eprint={2605.29398},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.29398}, 
}
```