Marin 535B-A23B is 13% through training. This hero run would not be possible without the generous support of the Jen-Hsun and Lori Huang Foundation, which provided the funding for the compute (Coreweave). Thanks @JensenHuang for supporting open models!

🚀One of the biggest questions for AI agents is whether they can continue expanding their capabilities after deployment. Deployment brings the experience needed to keep improving. As @ilyasut has argued, future intelligent systems should learn from deployment. But this requires more than a new learning algorithm. It requires turning the serving stack itself into a learning layer: collecting live experience, turning it into updates, and safely bringing those updates back into serving. That’s why we built Reef. Reef is open-source infrastructure for continuously evolving agents at live deployment. To our knowledge, it is the first open-source infrastructure designed to evolve both model weights and the agent harness from deployment experience. Not just weights, but also prompts, memory, skills, tools, and orchestration. Reef already supports: 🧠Model evolution: SAO, TTT-Discover, OpenClaw-RL, with more recipes coming. 🛠️Harness evolution: SkillClaw, Meta-Harness, and a general harness-evolution engine built on Cordis, with native support for pi @pidotdev , OpenCode @opencode , and more harnesses coming. With Reef, inference is no longer the end of the pipeline. It becomes part of a continual loop: serve → learn → evolve → serve again. Reef is fully open source. We would love you to try it, build on it, and tell us what is missing! ⭐ GitHub: 💬 Discord: #AgenticAI# #llms#