NeMo-DCR: Bit-Exact Delta-Compressed Refit for Scalable Agentic RL at Trillion-Parameter Scale

NVIDIA's NeMo-DCR introduces bit-exact delta-compressed refit for agentic reinforcement learning at trillion-parameter scale, addressing the 87.5-minute transfer time for a full 1T checkpoint between two AWS regions. The technique targets weight synchronization between training and rollout clusters in disaggregated agentic RL pipelines.

Agentic reinforcement learning RL disaggregates training from rollout, so each policy update must reach the rollout clusters before the next batch. Transferring a full 1T checkpoint for such weight synchronization refit takes 87.5 min between two AWS regions. Measurements of BF16 training show t