Taking Reinforcement Learning Cross Datacenter — Nan Jiang, Modal
Aug 10, 2026 · 19:50
Nan Jiang from Modal explains how reinforcement learning post-training can run across datacenters by shipping sparse weight deltas instead of full checkpoints. He argues that less than 1% of rollout-visible weights change between versions because Adam steps are tiny relative to BF16 rounding boundaries, a mechanism he calls Adam absorption. Modal's implementation, Stitch, lets rollout engines sync via patches (e.g., 500 MB instead of 500 GB) and operate as an elastic fleet across regions and providers. He cites internal runs showing 0.15% weight changes initially, settling near 0.05% for GLM 4.7 Air in FP8, and notes gradients are dense but updates small. He also explores whether sparsity holds for Muon and async RL scalability.
