Introducing TRACE: deployment-time compute scaling for robot learning. From... a few Tutor demos, frontier LLM agents rebuild a robot's task in sim and train reliable, human-speed policies. Performance becomes a function of compute. TI-0.1 is our first model trained with TRACE.show more

First came pre-training scaling; then came inference-time scaling. Now... comes judge-time scaling. Despite progress in AI through scaled inference-time compute, AI remains unreliable in open-ended, non-verifiable domains. The key limitation is not generation—it is evaluation. Therefore, the next big leap for AI comes from better judging. In service of this future, today we release Verdict, a library for scaling judge-time compute.show more

How can we leverage diverse human videos to improve... robot manipulation? Excited to introduce EgoVLA — a Vision-Language-Action model trained on egocentric human videos by explicitly modeling wrist & hand motion. We build a shared action space between humans and robots, enabling seamless transfer. With some robot demos, EgoVLA becomes a powerful, generalizable robot policy.show more

CATHIE WOOD CALLS OPTIMUS HUMAN-LEVEL BY 2028 ON 1GW... COMPUTE SURGE ARK Invest's 2026 robotics deep dive just laid out a bold timeline: Tesla Optimus reaching human-level task proficiency around 2028, riding a sustained AI compute explosion to 1.08 gigawatts. The scaling curve ties FSD compute ramps to performance jumps, projecting the humanoid cracking everyday complexity to match us mortals. Source: Nic Cruz Patane, TESLA CARS ONLY⚡️show more

Being able to trace and visualize how a model... is executing in our engine just hits differentshow more

Introducing DeepThought-8B: Transparent reasoning model built on LLaMA-3.1 with... test-time compute scaling. - JSON-structured thought chains & controllable inference paths. - ~16GB VRAM, competitive w/ 70B models. - Open model weights, and inference scripts.show more

🚨Current scalable RL algos train a policy w/o value... func, which is limiting with learning in open-ended, non-stationary, dynamic environments. But, how to scale value-based RL with more data/compute is unclear... Not anymore: presenting scaling laws for value-based RL 🧵⬇️show more

NVIDIA announces the first open humanoid robot reference design... built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:show more

Synthetic rewrites are now standard to improve data quality... and when data is limited. But how much compute should you spend rewriting? And can a model trained on rewrites beat the rewriting model? We fit scaling laws to answer both: 1/🧵show more

New in Chrome DevTools! Debug an app's full performance... trace with Gemini! After recording a trace, you can now chat with Gemini about the entire trace, related Performance insights, and even connected field data - all without needing to select specific context beforehand!show more

LayerAI Compute: Decentralized GPU Power for AI 🧬 LayerAI... is stepping into the physical AI infrastructure game with our latest: DePin for AI Computing. This move tackles a key AI/ML market gap: instant, on-demand compute power, available worldwide with just a few clicks. We're not just decentralizing data with AI2Earn; we're now also decentralizing the compute, making the AI ecosystem stronger and more resilient. The first iteration of this product will go live in the 2nd week of February.show more

Trained in Simulation Our robot learns to walk naturally... similar to a human via a high fidelity physics simulator We simulate years of data in only a few hoursshow more

Collective agent compute, on-demand for your task. A swarm... of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.show more

RLDX-1 a dexterity-first foundation model for robot hands, is... coming in a few days. It is interesting.show more

Introducing Akari: a frontier learned rendering model for robotics... simulation. Akari generates photorealistic rendering for any physics engine to close the sim-to-real gap. Trained in-house, it is the fastest and highest-fidelity learned renderer in any sim platform: 0.046 LPIPS at 0.5s/frame, consistent across 1K+ frames.show more

Turns out you can train humanoid hands without any... robot data. The idea in HUG is quite simple: (a) collect human data with smart glasses, (b) train a human manipulation model, (c) retarget to multi-fingered robot hands.show more

NVIDIA's Jim Fan has verified from reliable sources that... the video by EngineAI is a real robot demo and not CGI. The controller is a neural net trained in the NVIDIA Isaac simulator using reinforcement learning. There’s also another video from a different angle.show more

My first task for the National Design Studio was... to make Rampart smaller. It's our frontier on-device privacy model trained to filter all personal user data before it even gets to After a lot of hill-climbing and data labeling, I'm excited to launch Rampart Edge along with Edward Coristine. So small the weights fit in a notebook. This will noticeably speed up the load time of for hundreds of thousands of Americans. Under budget and ahead of schedule!show more

This also shows up in the representations learned by... the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.show more

Track2Act: Our latest on training goal-conditioned policies for diverse... manipulation in the real-world. We train a model for embodiment-agnostic point track prediction from web videos combined with embodiment-specific residual policy learning 1/nshow more