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This software project accompanies the research paper, DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation.
[Paper](https://arxiv.org/pdf/2508.13148) [Project](https://cli212.github.io/MDPO/)
The implementation of "Addressing the Training-Inference Discrepancy in Discrete Diffusion for Text Generation"
Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models? A comprehensive framework for transferring alignment knowledge from AR-LLMs to Diffusion Models
The main features of amap-ml/ar-map are: Training and Alignment.
Projects with overlapping indexed features include: apple/ml-diffucoder — This software project accompanies the research paper, DiffuCoder: Understanding and Improving Masked Diffusion Models… autonomousvision/mdpo — [[Paper]](https://arxiv.org/pdf/2508.13148) [[Project]](https://cli212.github.io/MDPO/). bhmingyang/tad — ```bash conda create -n tad python=3.10 -y conda activate tad. bowen-sun-0728/blockwise-sft — Blockwise SFT for Diffusion Language Models: Reconciling Bidirectional Attention and Autoregressive Decoding. chenyuwang-monica/drakes — The repository contains the code for the DRAKES method presented in the paper: Fine-Tuning Discrete Diffusion Models… aistairc/text-diff-2step-loss — The implementation of "Addressing the Training-Inference Discrepancy in Discrete Diffusion for Text Generation".
