Federico Cassano
Federico Cassano is a researcher and Research Lead at Cursor (Anysphere), where he leads development of Composer 2, the company's proprietary agentic coding model. He is best known for architecting a globally distributed reinforcement learning training system built in collaboration with Fireworks AI, which disaggregates training and inference across multiple clusters and syncs weight deltas rather than full model checkpoints. Prior to Cursor, he conducted academic research at Northeastern University's PRL lab under Arjun Guha and held positions at Scale AI, Roblox, and Trail of Bits.
“Every serious AI application company will eventually need to train its own models to compete. Prompt engineering and off-the-shelf models hit a ceiling — the real leverage is baking your application's specific behavior, tools, and environment directly into model weights.”
Source→“Cursor and Fireworks built a globally distributed system where training and inference are disaggregated across multiple clusters — including repurposing production inference GPUs during off-peak hours — and syncing weight deltas (not full models) across the globe.”
Source→“We started from a very strong base, which is Kimi 2.5. That's like a one trillion parameter MOE that's 30B active. So very, very sparse, actually.”
Source→“There is actually this kind of myth that during RL, you spend more, way more inference flops than training flops. This is sort of like just because the open source inference engines are very unoptimized instead of actually being a property of RL. Roughly the same ratio... In theory, if you push the GPUs to the maximum, you should have one third of your training GPUs allocated to inference.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.