提出并行多模态扩散模型，解决推理生成中图文不一致问题。
MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation
- 采用并行扩散框架实现文本与图像全程双向交互
- 在ParaBench上提升6.9%输出对齐度，优于当前最优模型
- 适合需要高一致性图文生成的应用场景
尽管思考感知生成旨在提升复杂任务表现，但现有顺序自回归方法存在错误传播导致性能下降的缺陷。为此，我们提出ParaBench基准，用于评估文本与图像输出模态。分析显示，性能下降与生成推理和最终图像之间的对齐度差强相关。为此，我们提出并行多模态扩散框架MMaDA-Parallel，支持在整个去噪轨迹中持续双向交互。该模型经监督微调后，进一步通过平行强化学习（ParaRL）优化，沿轨迹施加语义奖励以强化跨模态一致性。实验验证，本模型显著提升跨模态对齐与语义一致性，在ParaBench上输出对齐度较最优模型Bagel提升6.9%，建立更稳健的思考感知图像合成范式。代码已开源。
原文摘要 · Abstract (English)
While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. To systematically analyze this issue, we propose ParaBench, a new benchmark designed to evaluate both text and image output modalities. Our analysis using ParaBench reveals that this performance degradation is strongly correlated with poor alignment between the generated reasoning and the final image. To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory. MMaDA-Parallel is trained with supervised finetuning and then further optimized by Parallel Reinforcement Learning (ParaRL), a novel strategy that applies semantic rewards along the trajectory to enforce cross-modal consistency. Experiments validate that our model significantly improves cross-modal alignment and semantic consistency, achieving a 6.9\% improvement in Output Alignment on ParaBench compared to the state-of-the-art model, Bagel, establishing a more robust paradigm for thinking-aware image synthesis. Our code is open-sourced at https://github.com/tyfeld/MMaDA-Parallel
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