Introducing CQN: Coarse-to-fine Q-Network, a value-based RL algorithm for... continuous control🦾Initialized with 20~50 demonstrations, it learns to solve real-world robotic tasks within 10 mins of training, without any pre-training and shaped rewards! (1/4)show more

Does off-policy value-based RL scale? In LLMs, larger scale... predictably improves performance. Value-based RL learns from arbitrary data and is sample-efficient, but folk wisdom says it doesn't scale 🧵⬇️We show predictability for scaling value-based RL!show more

Introducing 2️⃣ new AI systems for robotics: 🤖 ALOHA... Unleashed to perform two-armed manipulation tasks 🦾 DemoStart to control a multi-fingered robotic hand They learned to tackle a range of actions requiring dexterity. Here's how. 🧵show more

How can non-experts quickly teach robots a variety of... tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a **single hand demo**, HAND learns manipulation tasks in under **4 minutes**! 🧵show more

Introducing First Dollar The new internet is being built... by us. One without geographical boundaries. Here you provide value to the network and it rewards you.show more

Introducing RL Environment Creator Skill Now any one can... create RL environments $ npx skills add adithya-s-k/RL_Envs_101 > You can create environments across multiple frameworks like OpenEnv, OpenReward, Verifiers, NemoGym ... > the repo has live working examples of environments that your coding agent can reference > The skill is design to first understand what type of model you are training and create an environment while keeping that in mind ps. There’s a lot more to building RL environments that can be used for training. One major aspect is the data, which this skill can’t directly solve. However, the skill will help with implementing tools, rewards, and other components of an RL environment, making it easier to go from idea to implementation quickly across different frameworks. Let me know if you’d be interested in a detailed, end-to-end blog/tutorial on building an environment and actually training a model for a useful use case.show more

New project! Flow Policy Gradients for Robot Control tldr;... a simple online RL recipe for training and fine-tuning flow policies for robots co-led w/ Hongsuk Benjamin Choi:show more

A hack to fine-tune your 𝕏 Algorithm: Engage with... and share links to posts that genuinely interest you, the algorithm learns from your actions.show more

Media gathering for world champions Argentina training with 40... mins to go before players due. #FIFAWorldCupshow more

Announcing our commercial partnership with Booster Robotics Booster builds... humanoid robot hardware, OS, and developer tools to make humanoid robots more affordable, reliable, and practical. The partnership centers on using simulation to multiply the value of real-robot data—expanding teleoperation demonstrations into scalable training data across diverse tasks and scenes. This joint effort powers sim-real co-training and foundation-model development, accelerating progress from hardware iteration to deployable robot policies. Together, we're building simulation‑powered data infrastructure for Physical AI — making scalable training data accessible to model developers and the broader robotics ecosystem.show more

Introducing autoresearch for arXiv papers: replicate and experiment on... any arXiv paper using an army of Claude/Codex agents For post-training, your agents can use Tinker to launch concurrent RL runs while you watch your experiment tree grow Try it out now on OpenResearch:show more

World BanksPi developer and Korea Ecosystem Ambassador Carter Lim... conducted 4 hours of training in Busan on the importance of ecosystem development, various tasks to prepare for the upcoming open mainnet, and the concept of GCV. We also conducted training on Dex, token, and equity pool. In the future, he will travel around the country to conduct such training.show more

1/ While most RL methods use shallow MLPs (~2–5... layers), we show that scaling up to 1000-layers for contrastive RL (CRL) can significantly boost performance, ranging from doubling performance to 50x on a diverse suite of robotic tasks. Webpage+Paper+Code:show more

Introducing the Move Alliance This first-of-its-kind ecosystem flywheel fuses... $MOVE buybacks with performance incentives that benefits the builders, the community, and the Movement network. Here's how the Alliance works: - Ecosystem companies commit a portion of their protocol revenue to transparent, on-chain $MOVE buybacks - Liquidity of this foundational chain token and network value compound - App usage and revenue grow and are rewarded with performance-based $MOVE incentives - Ever increasing buybacks, usage-based incentives, and growth fuel a virtuous cycle of mutually beneficial ecosystem and network upside. Within the Alliance, ecosystem teams defer TGEs and instead earn performance-based $MOVE incentives without their own token overhead and drag. A new blueprint. For sustainable value. That lifts the entire community. Welcome to the Move Alliance.show more

Today we’re launching something bigger than cashback. Introducing the... xMoney Rewards Network 🔥 A new system for how value moves - where spending, staking, and rewards work together. Let’s get into it 🧵show more

Generalist robot policies need a benchmark that works across... any robot and any policy. 🦾 Introducing RoboLab, a high‑fidelity simulation benchmark built on NVIDIA Isaac and Omniverse to evaluate generalist robot policies in diverse, photoreal, physics‑based environments. Coming soon to the NVIDIA Isaac Lab‑Arena roadmap for large‑scale, robotic policy evaluation. 📖 #NationalRoboticsWeekshow more

New research from Databricks: LLMs Can Learn to Reason... via Off-Policy RL Optimal Advantage-based Policy Optimization with Lagged Inference policy (OAPL) shows you don’t need strict on-policy training to improve reasoning. It matches or beats Group Relative Policy Optimization (GRPO), stays stable with large policy lag, and uses ~3× fewer training generations. For Databricks customers, it’s a simpler, practical, and equally powerful approach to RL that Databricks is pioneering internally — and bringing directly to Databricks customers, so enterprises can improve agents using the same methods we use for our in-house agents, without complex infrastructure changes.show more

Figure is aiming to develop the world’s largest and... most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.show more

Introducing “FlowCam: Training Generalizable 3D Radiance Fields w/o Camera... Poses via Pixel-Aligned Scene Flow”! We train a generalizable 3D scene representation self-supervised on datasets of raw videos, without any pre-computed camera poses or SFM! 1/nshow more