DARE: Diffusion Large Language Models Alignment and Reinforcement Executor
Diffusion large language models (dLLMs) are emerging as a compelling alternative to dominant autoregressive models, replacing strictly sequential token generation with iterative denoising and parallel generation dynamics. However, their open-source ecosystem remains fragmented across model families and, in particular, across post-training pipelines, where reinforcement learning objectives, rollout implementations and evaluation scripts are often released as paper-specific codebases. This fragmentation slows research iteration, raises the engineering burden of reproduction, and makes fair comparison across algorithms difficult. We present \textbf{DARE} (\textbf{d}LLMs \textbf{A}lignment an...
| Authors | Jingyi Yang, Yuxian Jiang, Xuhao Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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