Optimizing delta weight syncs for managed rollouts

Baseten describes a managed rollout system for reinforcement learning that synchronizes compressed delta weight updates from customer-controlled trainers to independent global GPU clusters without requiring shared networks, schedulers, memory, or direct trainer-to-GPU connections. Trainers publish full checkpoints and XOR-based byte-level weight deltas to object storage, while the Baseten Delivery Network distributes the data so clusters download weights once and reconstruct policies locally; in a GLM-5.3 fixture, 716.56 GiB of logical XOR data reportedly compressed to 1.55 GiB with zstd. To avoid decompressing and scanning the entire logical model output, the company developed a sparse zstd decoder that interprets compression commands directly, tracks zero runs, and emits only changed bytes and necessary repeated nonzero values. These changes are converted into retry-safe absolute tensor values, translated into vLLM’s GPU memory layout, and batched to reduce many small GPU operations into fewer transfers. Requests are paused only during the final GPU update, which Baseten says reduced the pause on an 8×B300 replica from roughly 12 seconds to 6 seconds, while the full publication-to-new-policy process averages about 36 seconds and checkpoint promotion continues asynchronously after service resumes.

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