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README.md

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+---
+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}, 
+}
+```