Recent advances in large-scale diffusion models have revolutionized generative AI across multiple domains, from image synthesis to audio generation, 3D asset...

Recent advances in large-scale diffusion models have revolutionized generative AI across multiple domains, from image synthesis to audio generation, 3D asset creation, molecular design, and beyond. These models have demonstrated unprecedented capabilities in producing high-quality, diverse outputs across various conditional generation tasks. Despite these successes…

Source

We keep seeing LLMs with larger context windows in the news, along with promises that they can hold entire conversation histories, volumes of books, or multiple...

We keep seeing LLMs with larger context windows in the news, along with promises that they can hold entire conversation histories, volumes of books, or multiple codebases in view at once. And yet, these models still repeat the same mistakes. We still have to copy and paste the earlier context back into the chat for LLMs to “get it”. A smart co-worker would pick up on these patterns, adapt…

Source

Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets...

Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets for training can be expensive, time-intensive, and dangerous. NVIDIA Cosmos open world foundation models (WFMs) address these challenges by enabling scalable, high-fidelity synthetic data generation for physical AI and the augmentation of…

Source

When training large language models (LLMs) with reinforcement learning from verifiable rewards (RLVR), one of the most compelling questions is how to overcome...

When training large language models (LLMs) with reinforcement learning from verifiable rewards (RLVR), one of the most compelling questions is how to overcome performance plateaus. The previous NVIDIA Research solution, Prolonged Reinforcement Learning (ProRL), showed that adding more reinforcement learning (RL) steps during prolonged training could expand the reasoning boundaries of LLMs.

Source