NVIDIA 用 Reshard Collective 加速强化学习权重重载
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
NVIDIA 提出 Reshard Collective，用于加速强化学习后训练中训练 GPU 向推理 GPU 的权重重载。在推理与训练分离部署的架构下，策略模型仅在训练侧更新，但推理侧需定期刷新权重，该方案针对这一环节做优化。
During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model. For...
During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model. For training efficiency, it is common to run inference and training in parallel on separate GPU resources in a disaggregated setup. Although the policy model is updated only on the training GPUs, the system must periodically refresh the weights on the…
来源：NVIDIA Technical Blog · developer.nvidia.com