---
library_name: transformers
pipeline_tag: text-generation
license: mit
---

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

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

GDSD is a reinforcement learning (RL) framework designed to improve the denoiser of diffusion large language models (dLLMs). It reduces RL to a likelihood-free self-distillation objective by matching the dLLM's denoiser logits to an advantage-guided self-teacher. This approach bypasses the training–inference mismatch (TIM) biases common in ELBO-based methods and leads to more stable training dynamics.

## Resources

- **Paper:** [GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models](https://arxiv.org/abs/2605.29398)
- **GitHub Repository:** [GaryBall/GDSD](https://github.com/GaryBall/GDSD)

## Citation 

If you find GDSD helpful, please consider citing the following work:

```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}, 
}
```