Incredible work, and very cool to see @periodiclabs using @raydistributed.

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

How does one RL post-train a 397B model for long-horizon knowledge work? 👩‍💼 We share every step we took to bring Qwen 3.5 397B from 16.1% Pass@1 to 27.3% on APEX-Agents using DPPO, including final models weights and the full training script🚀 This is the first of many works from Mercor Research on open model training research. Full blog: mercor.com/blog/training-fro… Source code: github.com/Mercor-Intelligen…

I got some as well. Inspirational talk! Also, very cool to see that @periodiclabs is using Kimi to reduce inference costs 20-50x over frontier APIs. Everyone will do this.