Block Diffusion Language Models (BD-LMs) support KV caching and flexible-length generation, but their native Single-Block Diffusion (SingleBD) inference leaves inter-block parallelism unused. We study Multi-Block Diffusion (MultiBD), where a running-set of consecutive blocks is refined concurrently. Existing teacher-forced BD-LMs observe only one noisy block conditioned on a clean prefix, while diffusion forcing exposes a long noisy suffix whose state distribution still differs from practical MultiBD inference. We bridge this gap with Multi-Block Diffusion Language Models (MBD-LMs), obtained by post-training BD-LMs using Multi-block Teacher Forcing (MultiTF). MultiTF trains on bounded noise-groups conditioned on clean prefixes, with heterogeneous slot-wise noise patterns that better match inference. An optimized Block Buffer further preserves prefix-cache reuse and static input shapes, translating inter-block parallelism into wall-clock acceleration. On math and code benchmarks, MBD-LLaDA2-Mini increases average Tokens Per Forward pass (TPF) from 3.47 to 6.19 while maintaining comparable accuracy (80.0% versus 81.0%). Combined with DMax, it reaches 9.34 TPF with a 1.02-point accuracy drop. Project Page: https://sjtu-deng-lab.github.io/mbd-lms

The deployment of Large Language Models (LLMs) in content generation raises significant safety concerns, particularly regarding the transparency and interpretability of content evaluations. Current methods, primarily focused on binary safety classifications, lack mechanisms for detailed critique, limiting their utility for model improvement and user trust. To address these limitations, we introduce SAFETY-J, a bilingual generative safety evaluator for English and Chinese with critique-based judgment. SAFETY-J utilizes a robust training dataset that includes diverse dialogues and augmented query-response pairs to assess safety across various scenarios comprehensively. We establish an automated meta-evaluation benchmark that objectively assesses the quality of critiques with minimal human intervention, facilitating scalable and continuous improvement. Additionally, SAFETY-Jemploys an iterative preference learning technique to dynamically refine safety assessments based on meta-evaluations and critiques. Our evaluations demonstrate that SAFETY-J provides more nuanced and accurate safety evaluations, thereby enhancing both critique quality and predictive reliability in complex content scenarios. To facilitate further research and application, we have released SAFETY-J’s training protocols, datasets, and code at https://github.com/GAIR-NLP/Safety-J.