🎉 Really excited, our paper "XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning" has been accepted at #ICLR2026. If you are interested in reinforcement learning, sample-efficiency, compute-efficiency go check it out. See you in Rio!

I'm super excited to have been named an #NVIDIA Graduate Fellowship Finalist! 💚 Huge thanks to my supervisor @jan-peters.bsky.social and all my collaborators. Can't wait to join the NVIDIA Seattle Robotics Lab for my internship next summer! 🤖 blogs.nvidia.com/blog/graduat...

Had a really great time presenting our #NeurIPS paper at the poster session today. Thanks to everyone who stopped by. If you are interested in sample-efficient #RL, check out our work: Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

🚀 New preprint! Introducing XQC— a simple, well-conditioned actor-critic that achieves SOTA sample efficiency in #RL ✅ ~4.5× fewer parameters than SimbaV2 ✅ Scales to vision-based RL 👉 arxiv.org/pdf/2509.25174 Thanks to Florian Vogt @joemwatson.bsky.social @jan-peters.bsky.social

🚀 New preprint "Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization"🤖 We propose CrossQ+WN, a simple yet powerful off-policy RL for more sample-efficiency and scalability to higher update-to-data ratios. 🧵 t.co/Z6QrMxZaPY #RL @ias-tudarmstadt.bsky.social

Check out our latest work, where we train an omnidirectional locomotion policy directly on a real quadruped robot in just a few minutes based on our CrossQ RL algorithm 🚀 Shoutout @nicobohlinger.bsky.social, Jonathan Kinzel. @ias-tudarmstadt.bsky.social @hessianai.bsky.social