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 DARE (dLLMs Alignment and Reinforcement Executor), an open framework for post-training and evaluating dLLMs. Built on top of verl~sheng2024hybridflow and OpenCompass~2023opencompass, DARE unifies supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and dLLM-specific reinforcement learning under a shared execution stack for both masked and block diffusion language models. Across representative model families including LLaDA, Dream, SDAR, and LLaDA2.x, DARE provides broad algorithmic coverage, reproducible benchmark evaluation, and practical acceleration. Extensive empirical results position that DARE serves as a reusable research substrate for developing, comparing, and deploying post-training methods for current and emerging dLLMs.
69d625d7c6ed9968ac5c6600
Jingyi Yang
claimed_verified
2026-04-08T11:41:01.072Z
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/avatars/328302a495de6a4418be835456d1d3c6.svg
jingyi Yang
JY-Young
user
JY-Young
69d625d7c6ed9968ac5c6601
Yuxian Jiang
69d625d7c6ed9968ac5c6602
Xuhao Hu
claimed_verified
2026-04-13T07:34:47.147Z
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XuHao Hu
Foreshhh
user
Foreshhh
69d625d7c6ed9968ac5c6603
Shuang Cheng
69d625d7c6ed9968ac5c6604
Biqing Qi
69d625d7c6ed9968ac5c6605
Jing Shao
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 DARE (dLLMs Alignment and Reinforcement Executor), an open framework for post-training and evaluating dLLMs. Built on top of verl~sheng2024hybridflow and OpenCompass~2023opencompass, DARE unifies supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and dLLM-specific reinforcement learning under a shared execution stack for both masked and block diffusion language models. Across representative model families including LLaDA, Dream, SDAR, and LLaDA2.x, DARE provides broad algorithmic coverage, reproducible benchmark evaluation, and practical acceleration. Extensive empirical results position that DARE serves as a reusable research substrate for developing, comparing, and deploying post-training methods for current and emerging dLLMs.
Diffusion large language models are gaining attention as alternatives to autoregressive models, utilizing iterative denoising and parallel generation instead of sequential token processing, yet their open-source landscape is fragmented, prompting the development of a unified framework for post-training and evaluation.
diffusion large language models autoregressive models sequential token generation iterative denoising parallel generation dynamics post-training pipelines reinforcement learning preference optimization parameter-efficient fine-tuning supervised fine-tuning masked diffusion language models block diffusion language models