Nebius cut rollout collection time by 66.9% — here’s what changed
In AI agent training, you can pay for powerful GPUs and still spend much of your time waiting. Agents need to attempt tasks and produce results before the model can learn from them. A few slow attempts can hold up the next training update, making the systems that run those tasks a major factor in training speed and cost. Di Jin, VP of AI Training at Amsterdam-headquartered Nebius, says teams coming from pretraining or supervised fine-tuning can underestimate the work these systems do. “If you staff and design it as a training project, inference becomes the bottleneck nobody owns.” Rollout generation often takes most of a training step’s wall-clock time, making batching, cache management and scheduling central to how fast the model can learn. “If you staff and design it as a training project, inference becomes the bottleneck nobody owns,” Jin says. He adds that engineers should measure…
