# Weight Refit

In reinforcement learning post-training, training and inference run on separate GPUs, and the latest weights must be moved from the training side to the inference side at every step; this transfer is called refit. The conventional approach all-gathers the complete parameter and then broadcasts it, so an inference GPU receives the full tensor even when it needs only a shard, giving a payload of roughly twice the model's weights. NVIDIA recasts refit as resharding a distributed tensor between the training and inference layouts and releases NCCL M2N Reshard as an open-source reference implementation; in NeMo RL benchmarks, refit latency falls by 2.7x to 5.7x relative to gather-and-broadcast refit.

In reinforcement learning post-training, training and inference run on separate GPUs, and the latest weights must be moved from the training side to the inference side at every step; this transfer is called refit. The conventional approach all-gathers the complete parameter and then broadcasts it, so an inference GPU receives the full tensor even when it needs only a shard, giving a payload of roughly twice the model's weights. NVIDIA recasts refit as resharding a distributed tensor between the training and inference layouts and releases NCCL M2N Reshard as an open-source reference implementation; in NeMo RL benchmarks, refit latency falls by 2.7x to 5.7x relative to gather-and-broadcast refit.