Frontier labs such as @OpenAI have shifted to prioritizing large-scale #RL training and expected the RL compute to significantly exceed what’s been spent on pre-training compute today. In this blog, I went deep dive into why RL has become one of the major focuses for them. I also share some thoughts on the 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘀𝗵𝗶𝗳𝘁 required to support modern RL training workloads and why 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 and 𝘁𝗲𝘀𝘁-𝘁𝗶𝗺𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 might be the key to solving long-tail and out-of-distribution problems. At @FusionFundVC, we back early-stage startups building next-generation RL infrastructure and tackling challenging problems that new RL advancements can unlock. If you're building in this space, I'd love to hear from you! charlottexia.substack.com/p/… #reinforcementlearning #RLinfra #ContinualLearning @grok

Our latest industry report by @xia_char & Matthew Wong explores whether #worldmodels can become the foundation for #EmbodiedAI. We believe the stack is still evolving, with near-term value emerging in data, evaluation, generalization, & infrastructure. 🔗bit.ly/3QibTof

We're hosting a small private event at BIO San Diego Only 20 spots in total Join @viswacolluru, @Jack_O_Meara and me to discuss AI, bio/acc and clinical timeline compression First event in a new series - very few spots left this time, rsvp below

Excited to cohost this one with @shinshin_oob @daikonland @browserbase!

Soon we will live forever! @MatttBuckley @WernigLab

Excited to share two recent works pushing the frontier of 3D perception for physical AI: 🌟 #MPA3D (#CVPR2026 Spotlight) introduces a novel 3D perception model that leverages surfels and 3D Gaussian Splatting (3DGS) to provide rich scene context, removing the dependency on HD maps. Paper: arxiv.org/pdf/2605.22997 📈 #STELLAR is a new #FoundationModel for 3D world perception in physical autonomy. By combining pre-training, mid-training, and fine-tuning, and scaling across data, model size, and tasks, #STELLAR sets a new SOTA on the Waymo Open Dataset benchmark. Paper: arxiv.org/pdf/2605.20390 Huge thanks to @yingwei_li @yangliux1 @yangfu21 @ylzou_Zack and all our incredible co-authors behind these works! Come stop by our poster if you’re attending CVPR next week. #CVPR #AutonomousDriving #PhysicalAI

Congrats @gabyllorenzi and the Primary team for launching this, and proud to see our portfolio @EriduAi being featured—Eridu is building the next generation AI networking technology to increase performance and GPU utilization while massively lowering power and cost.

We are hosting a closed-door discussion on Generative Modeling via Drifting with Mingyang Deng @Goodeat258 on May 16. 🚀 🔗 RSVP here: luma.com/bldsrr7j Special thanks to @AndrewDai @yinfeiy and @ElorianAI team for their support and sponsorship of this event.

The deeper implication is that VLAs are better understood as a clever transitional crutch when high quality data is scarce. What determines model capability is the #quantity, #quality and #diversity of the physical-interaction corpus and how the VLA is conditioned (language subtasks, visual subgoals, strategy metadata, embodiment tags), where world models can play a big role in, to compose skills across bodies and scenes.

The bitter lesson learned was that as data was scaled, the gap between a world-model policy and an VLA largely disappeared. In parallel, Generalist’s GEN-1 is neither a VLA nor a world model (what's the definition of world model anyway?), roughly 99% of its parameters are trained from scratch on over half a million hours of #UMI-style physical interaction data, setting a precedent for UMI-style data scaling as the new substrate for robotics foundation models.