d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning A two-stage approach combining masked SFT with diffu -GRPO—a novel policy gradient method based on GRPO that features efficient log probability estimation designed for masked dLLMs—to scale reasoning capabilities…
dllm-reasoning/d1 的主要功能包括：Training and Alignment。
dllm-reasoning/d1 的开源替代品包括： amap-ml/ar-map — Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models? A comprehensive… 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. aistairc/text-diff-2step-loss — 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
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"