Add model card for GDSD

This PR adds a model card for the GDSD checkpoint. It includes:
- Metadata for `pipeline_tag` and `library_name`.
- Links to the paper [GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models](https://huggingface.co/papers/2605.29398).
- A link to the official GitHub repository.
- The BibTeX citation for researchers.

README.md

@@ -0,0 +1,29 @@
+---
+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}, 
+}
+```