Introduction to Training Ai On The Scientific Process Liam Fedus Periodic Labs Ray Summit 2026
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Building an AI Physicist: ChatGPT Co-Creator’s Next Venture
Periodic Labs: Training AI Scientists, with Liam Fedus & Ekin Dogus Cubuk (from a16z)
Reward Stress Tests for Agentic Post-Training with Ray | Prime Intellect | Ray Summit 2026
TorchSpec: Speculative Decoding Training at Scale | Together AI | Ray Summit 2026
Foundation Model Distributed Training with Ray | Anyscale | Ray Summit 2026
Evolving and Scaling Ray for the Next Generation of AI Workloads | Google | Ray Summit 2026
Ray Train on Anyscale: Observability and Reliability at Scale | Anyscale | Ray Summit 2026
Diffusion Large Language Models | Inception Labs | Ray Summit 2026
Scaling Physical AI & Robotics Systems with Ray | Anyscale | Ray Summit 2026
Keynote: AI Scientists: Automating Scientific Discovery | Day 2
Leveraging Agent Skills for Ray Workloads | Anyscale | Ray Summit 2026
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Last Updated: October 3, 2026
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At Accel's GPU Mode in San Francisco, What happens when you apply the scaling laws of large language models to the physical work of atoms? Elad Gil sits down with ... Scaling laws took us from GPT-1 to GPT-5 Pro. But in order to crack physics, we'll need a different approach. In this episode, a16z ... Today's special crosspost features General Partner Anjney Midha with TorchSpec is a torch-native speculative decoding "Github repository: github.com/anyscale/ Modern models can span thousands of GPUs and months of Mercury 2 is the first reasoning model built on a diffusion architecture and the fastest model on the Artificial Analysis index.
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