Looking forward to representing the Open-Endedness research community with a keynote at ICLR 2026 2025 in Singapore! 🚀

🚨 Schedule drop! 🚨 The World Models Workshop @ #ICLR2025 is coming🔥! Mark your calendars 🗓️ for April 28th 08:00-18:00.🕗 sites.google.com/view/worldmode… We’re excited to welcome a fantastic lineup of speakers and panellists from across #Robotics, #RL, #GenerativeAI, and #Causality

I just arrived in Singapore for ICLR, where I will present: - OMNI-EPIC poster on Fri 25 Apr 15:00-17:30 - CAX poster on Sat 26 Apr 10:00-12:30 I am super happy that CAX got accepted at ICLR 2025 as an oral presentation. 🤩 If you are interested in open-endedness/artificial

Presenting Intelligent Go-Explore at poster #282 now! Shengran Hu Jeff Clune

Thrilled that we have been recognized as a Top 100 AI startup! So proud of our team. ✨

We will be presenting OMNI-EPIC today afternoon at #ICLR2025 ! Do drop by to say hi, ask questions, or give any feedback 🤩👂 work done together with Maxence Faldor, Antoine Cully, Jeff Clune

Several people from the AIRL lab and I are at ICLR'25 this week. Come to see our latest work and have a chat. See below for the full program!

Had a great time at #ICLR2025, especially at the Robot Learning Workshop, where our paper Instant Policy (w/ Edward Johns @ CoRL 2025) won the Best Paper Award! It's an exciting time for in-context learning and robotics! 🦾 robot-learning.uk/instant-policy

Harder, Better, Faster, Stronger, Real-time! We are excited to reveal Genie 3, our most capable real-time foundational world model. Fantastic cross-team effort led by Jack Parker-Holder and Shlomi Fruchter. Below some interactive worlds and capabilities that were highlights for me

Almost exactly 10 years after joining Imperial College London as a Postdoc, I am honoured to announce that I am now Professor in Machine Learning and Robotics! 👨‍🎓 🤖 My fantastic team found the best gift to celebrate this special occasion!

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: sakana.ai/shinka-evolve/ Code: github.com/SakanaAI/Shink… Like AlphaEvolve and its variants, our framework leverages LLMs to

The narrative around LLMs is that they got better purely by scaling up pretraining *compute*. In reality, they got better by scaling up pretraining *data*, while compute is only a means to the end of cramming more data into the model. Data is the fundamental bottleneck. You can't