Agentic reinforcement learning (RL) disaggregates training from rollout into different clusters, so each policy update must reach rollout clusters before the next batch: a full 1T checkpoint takes 87.5 min between two AWS regions, yet only 1-2% of BF16 elements change their stored values per step. Concurrent systems use this sparsity but reimplement placement rules, assemble full tensors, rebuild values arithmetically, or rely on a cross-cluster collective, and none fully recovers from mid-refit failures. We present NeMo-DCR (Delta-Compressed Refit), which sends only changes yet gives receivers the bits of a dense refit. NeMo-DCR describes changes in the Hugging Face canonical coordinates that both sides already share and leaves their placement to the serving runtime's native loader. It projects over 96% of MoE weight bytes directly and converts the rest, under 4%. It encodes changes as compressible XOR masks for over 96% of these bytes and as overwrites where XOR masks cannot apply, streams them over object storage or a relay tree without a cross-cluster collective, and updates receivers in place, recovering from failures with overwrite retries and a joint commit. With real updates, NeMo-DCR is $7\times$ faster than AReaL-DTE. In 3% and 5% stress cases, 30B-1T refits are 12-40$\times$ faster than a transport-only full-checkpoint reference over the same object storage, and would make a synchronous Kimi K2 RL step 1.4-1.5$\times$ faster at 1T.

翻译：暂无翻译