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library_name: transformers
pipeline_tag: text-generation
---
# GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
This repository contains the model weights for GDSD (Guided Denoiser Self-Distillation), as presented in the paper [GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models](https://arxiv.org/abs/2605.29398).
GDSD is a reinforcement learning (RL) framework for diffusion large language models (dLLMs) that bypasses the intractability of policy likelihood. It distills the denoiser of dLLMs from an advantage-guided self-teacher derived from the closed-form optimum of reverse-KL regularized RL. This method avoids the Training-Inference Mismatch (TIM) biases common in ELBO-based approaches, leading to more stable training and improved performance on planning, math, and coding benchmarks.
- **Paper:** [https://arxiv.org/abs/2605.29398](https://arxiv.org/abs/2605.29398)
- **Repository:** [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},
}
``` |