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.

Augmenting pretrained language models (LMs) with a vision encoder (e.g., Flamingo) has obtained state-of-the-art results in image-to-text generation. However, these models store all the knowledge within their parameters, thus often requiring enormous model parameters to model the abundant visual concepts and very rich text descriptions. Additionally, they are inefficient in incorporating new data, requiring a computational-expensive fine-tuning process. In this work, we introduce a Retrieval-augmented Visual Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the relevant knowledge from the external database for zero and in-context few-shot image-to-text generations. By storing certain knowledge explicitly in the external database, our approach reduces the number of model parameters and can easily accommodate new data during evaluation by simply updating the database. We also construct an interleaved image and text data that facilitates in-context few-shot learning capabilities.We demonstrate that Re-ViLM significantly boosts performance for image-to-text generation tasks, especially for zero-shot and few-shot generation in out-of-domain settings with 4x less parameters compared with baseline methods.