🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset

Atlas is demonstrating reinforcement learning policies developed using a motion capture suit. This demonstration was developed in partnership with Boston Dynamics and RAI Institute.

Introducing Gemini 2.5 Pro Experimental. The 2.5 series marks a significant evolution: Gemini models are now fundamentally thinking models. This means the model reasons before responding, to maximize accuracy -- and it’s our best Gemini model yet. Blog -

Thank you Jeff Clune!! 🙏

Simo Ryu So we do diffusion for text and AR for images in the future, got it

Rich Sutton just published his most important essay on AI since The Bitter Lesson: "Welcome to the Era of Experience" Sutton and his advisee Silver argue that the “era of human data,” dominated by supervised pre‑training and RL‑from‑human‑feedback, has hit diminishing returns;

[⏭️PyTorch impl of Shortcut Models] I released a tutorial code of Shortcut Models [Kevin Frans, 2025] for diffusion/flow-matching models with few step sampling. If you're interested in Few-step/fast sampling, please be sure to check it out!! Code: github.com/yukara-ikemiya…

I’m not just dancing all day, ok

1. We often observe power laws between loss and compute: loss = a * flops ^ b + c 2. Models are rapidly becoming more efficient, i.e. use less compute to reach the same loss But: which innovations actually change the exponent in the power law (b) vs change only the constant (a)?

📢 (1/16) Introducing PaTH 🛣️ — a RoPE-free contextualized position encoding scheme, built for stronger state tracking, better extrapolation, and hardware-efficient training. PaTH outperforms RoPE across short and long language modeling benchmarks arxiv.org/abs/2505.16381

Very excited for this one. We took a cautiously experimental view on NN optimizers, aiming to find something that just works. SPlus matches Adam within ~44% of steps on a range of objectives. Please try it out in your setting, or read below for how it works.

🚀Introducing GMT — a general motion tracking framework that enables high-fidelity motion tracking on humanoid robots by training a single policy from large, unstructured human motion datasets. 🤖A step toward general humanoid controllers. Project Website: