Diffusion language models (LMs) enable parallel decoding, but still lag behind autoregressive (AR) LMs in accuracy, and behind multi-token prediction (MTP) methods in efficiency. This raises three questions: should diffusion LMs compete with AR LMs or be harmonized with them; can diffusion provide a stronger acceleration mechanism than MTP; and does diffusion decoding have sufficient long-term potential to justify further exploration? We study these questions through Nemotron-Labs-Diffusion, a tri-mode LM that unifies three decoding modes—AR, diffusion, and self-speculation (diffusion drafts, AR verifies)—within a single model to sustain high throughput across all concurrency levels. Our study shows that (1) AR and diffusion objectives are mutually beneficial rather than competing, (2) diffusion-based self-speculation achieves higher acceptance lengths and better system vs. per-user throughput trade-offs than MTP baselines, and (3) diffusion decoding shows strong potential for parallel decoding under an optimal sampler in a speed-of-light analysis. Based on these insights, we develop the Nemotron-Labs-Diffusion family at 3B, 8B, and 14B scales. Our models outperform state-of-the-art open-source AR and diffusion LMs in both accuracy and speed, e.g., achieving 6x tokens per forward over Qwen3-8B with comparable benchmark accuracy, translating to 4x throughput on SPEED-Bench measured with SGLang on an NVIDIA GB200 GPU.

We present an effective recipe to train strong long-context LLMs that are capable of utilizing massive context windows of up to 32,000 tokens. Our models are built through continual pretraining from Llama 2 checkpoints with longer text sequences and on a dataset where long texts are upsampled. We perform extensive evaluation using language modeling, synthetic context probing tasks, and a wide range of downstream benchmarks. Across all evaluations, our models achieve consistent improvements on most regular-context tasks and significant improvements on long-context tasks over Llama 2. Moreover, with a cost-effective instruction tuning procedure that is free of expensive annotation, the presented models can already surpass gpt-3.5-turbo-16k‘s overall performance on long-context benchmarks. Alongside these results, we provide an in-depth analysis on each individual component of our method. We delve into Llama’s position encodings and discuss its key limitation in modeling long data. We examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths – ablation results suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.

3D human modeling has been widely used for engaging interaction in gaming, film, and animation. The customization of these characters is crucial for creativity and scalability, which highlights the importance of controllability. In this work, we introduce Text-guided 3D Human Generation (T3H), where a model is to generate a 3D human, guided by the fashion description. There are two goals: 1) the 3D human should render articulately, and 2) its outfit is controlled by the given text. To address this T3H task, we propose Compositional Cross-modal Human (CCH). CCH adopts cross-modal attention to fuse compositional human rendering with the extracted fashion semantics. Each human body part perceives relevant textual guidance as its visual patterns. We incorporate the human prior and semantic discrimination to enhance 3D geometry transformation and fine-grained consistency, enabling it to learn from 2D collections for data efficiency. We conduct evaluations on DeepFashion and SHHQ with diverse fashion attributes covering the shape, fabric, and color of upper and lower clothing. Extensive experiments demonstrate that CCH achieves superior results for T3H with high efficiency.