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A single training step produces tens of thousands of search requests across 1,024 trajectories executing tool calls in parallel, with peak concurrency at 3,000+ queries per second as environments cycle between model inference and tool execution. To handle this load, each Chroma collection is replicated internally on Chroma Cloud, with each tool call randomly selecting a replica. The replication factor was chosen empirically to keep query latency manageable under peak rollout load. No failures were observed from our search infrastructure during training, removing that as a potential source of instability during large scale parallel rollouts.