metadata
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.
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
Citation
If you find GDSD helpful, please consider citing:
@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},
}