$ cat accelerating-reinforcement-learning-weight-refit-with-the-reshard-collective.md
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
Learn how the Reshard collective speeds up weight refit in reinforcement learning post-training, where updated policy weights must be synced from training GPUs to separate inference GPUs in a disaggregated setup. The article covers why this transfer becomes a bottleneck and how the new approach reduces it.
NVIDIA Technical Blog07 Nov 2026 · 1 min read · summary
Read on developer.nvidia.com ↗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…
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