Directional Diffusion-Style Code Editing Pre-training
We propose a directional diffusion technique at the data level of code evolution and apply it within an Encoder-Decoder framework.
The directional diffusion can be mainly divided into the following two steps:
Noising process at the data level.
- Seting the starting point to old code.
- Adding artificial noise to perturb the old data.
- Exploring the intermediate step in the real evolution process.
Denoising process within the Encoder-Decoder framework.
- Using the auto-regressive nature of the Encoder-Decoder framework
- Perceiving the evolutionary direction in generation.
@article{liang2025directional,
title={Directional Diffusion-Style Code Editing Pre-training},
author={Liang, Qingyuan and Sun, Zeyu and Zhu, Qihao and Hu, Junhao and Zhao, Yifan and Chen, Yizhou and Zhu, Mingxuan and Wang, Guoqing and Zhang, Lu},
journal={arXiv preprint arXiv:2501.12079},
year={2025}
}
- Downloads last month
- 2
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
HF Inference API was unable to determine this model's library.