NVIDIA launches Reshard Collective to accelerate RL weight reloading
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NVIDIA proposes Reshard Collective to accelerate weight reloading from training GPUs to inference GPUs during reinforcement learning post-training. The solution targets scenarios where inference and training are deployed separately, addressing the need to periodically refresh inference-side weights after the policy model is updated. NVIDIA states that training trajectories in reinforcement learning post-training are generated by running inference in real time with the current policy model, so after the policy model is updated, the inference-side weights need to be refreshed in sync.
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- NVIDIA Technical BlogAccelerating Reinforcement Learning Weight Refit with the Reshard Collective
This post discusses how reinforcement learning post-training generates trajectories by running inference with the current policy model, and how disaggregated setups run inference and training in parallel on separate GPU resources for efficiency. Because the policy model is updated only on the training GPUs, the system must periodically refresh the weights on the inference side, and the post introduces the Reshard Collective to accelerate this weight refit.
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