an agent can write code in seconds. But installing dependencies, running tests and waiting for results can take MUCH longer When you’re training agents, all that waiting is important. Faster text generation only speeds up one part of the process. My colleagues Di and @Mhr1036 at @nebiusai worked on the rest: how tasks get routed, how tools run, and how completed work becomes a batch the model can learn from. One idea I love: use groups of completed attempts as they become ready, so unrelated slow tasks don’t hold everything up Across the changes, they reduced the time to collect a training batch from roughly 10 minutes to just over 3 in a controlled Terminal Bench based test, with model weights held fixed. That’s 66.9% less time gathering the next batch For me, this is an interesting part of building better AI: the engineering around the model determines how much of its speed becomes useful progress. Full breakdown below